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SAS HelpData Sets

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SAS Help Data Sets

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ContentsChapter 1. Sashelp Data Sets . . . . . . . . . . . . . . . . . . . . . . . . . 1

Chapter 1

Sashelp Data Sets

ContentsSashelp.aacomp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Sashelp.aarfm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5Sashelp.air — Airline Data (Monthly: Jan49-Dec60) . . . . . . . . . . . . . . . . . . . . . 6Sashelp.applianc — Sales Time Series for 24 Appliances By Cycle . . . . . . . . . . . . . . 7Sashelp.assocwa — Current Association List . . . . . . . . . . . . . . . . . . . . . . . . . 9Sashelp.baseball — 1986 Baseball Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Sashelp.bmt — Bone Marrow Transplant Patients . . . . . . . . . . . . . . . . . . . . . . . 13Sashelp.buy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15Sashelp.bweight — Infant Birth Weight . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16Sashelp.cars — 2004 Car Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17Sashelp.citiday — Citibase Daily Indicators: Jan88-Feb92 . . . . . . . . . . . . . . . . . . 19Sashelp.citimon — Citibase Monthly Indicators: Jan80-Jan92 . . . . . . . . . . . . . . . . 20Sashelp.citiqtr — Citibase Quarterly Indicators: 80: 1-91: 4 . . . . . . . . . . . . . . . . . 22Sashelp.citiwk — Citibase Weekly Indicators: Dec85-Jan92 . . . . . . . . . . . . . . . . . 24Sashelp.citiyr — Citibase New File Format . . . . . . . . . . . . . . . . . . . . . . . . . . 25Sashelp.class — Student Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26Sashelp.classfit — Predicted Weights With Confidence Limits . . . . . . . . . . . . . . . . 27Sashelp.comet — Comet Assay Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28Sashelp.company — Several Hierarchical Levels of a Company . . . . . . . . . . . . . . . 29Sashelp.demographics — Data Derived from World Population Prospects: the 2004 Revision 30Sashelp.electric — Electric Power Generation and Revenue . . . . . . . . . . . . . . . . . . 33Sashelp.eng_multi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36Sashelp.enso — El Nino Southern Oscillation . . . . . . . . . . . . . . . . . . . . . . . . . 37Sashelp.failure — MOS Capacitor Failure . . . . . . . . . . . . . . . . . . . . . . . . . . . 38Sashelp.fish — Measurements of 159 Fish Caught in Lake Laengelmavesi, Finland . . . . . 40Sashelp.frnch_multi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42Sashelp.ftable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43Sashelp.gas — Nitrogen Oxide Emissions from a Single Cylinder Engine . . . . . . . . . . 44Sashelp.gcdirect — Standard Street Direction Abbreviations for US Geocoding . . . . . . . 46Sashelp.gcstate — State/Province Names and Abbreviation for Geocoding . . . . . . . . . . 47Sashelp.gctype — Street Type Abbreviations for US Geocoding . . . . . . . . . . . . . . . 49Sashelp.geoexm — Primary Street Lookup Data for PROC GEOCODE (Tiger 2012) . . . . 50Sashelp.geoexp — Tertiary Street Lookup Data for PROC GEOCODE (Tiger 2012) . . . . . 52Sashelp.geoexs — Secondary Street Lookup Data for PROC GEOCODE (Tiger 2012) . . . 53Sashelp.germ_multi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

2 F Chapter 1: Sashelp Data Sets

Sashelp.gisimp — GISIMP: SAS/GIS Import Data Set . . . . . . . . . . . . . . . . . . . . 56Sashelp.gngsmp1 — Sample Chart Data for Graph-N-Go . . . . . . . . . . . . . . . . . . . 58Sashelp.gngsmp2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60Sashelp.gnp — GNP/Macro Data (Quarterly: 1960-1991) . . . . . . . . . . . . . . . . . . . 61Sashelp.gridded — Gridded Weight and Height . . . . . . . . . . . . . . . . . . . . . . . . 62Sashelp.gulfoil — Monthly Oil and Gas Production . . . . . . . . . . . . . . . . . . . . . . 63Sashelp.heart — Framingham Heart Study . . . . . . . . . . . . . . . . . . . . . . . . . . . 65Sashelp.holiday — Holiday Data for En US and En CA Locale . . . . . . . . . . . . . . . . 67Sashelp.humid — Source: W. L. Donn, Meteorology, 4th Edition . . . . . . . . . . . . . . . 68Sashelp.iris — Fisher’s Iris Data (1936) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69Sashelp.ital_multi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71Sashelp.ja . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72Sashelp.junkmail — Classifying Email As Junk Or Not . . . . . . . . . . . . . . . . . . . . 73Sashelp.ko . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77Sashelp.lake — Sample 3D Data of Lake Depth . . . . . . . . . . . . . . . . . . . . . . . . 78Sashelp.leutest — Leukemia Data Set - Validation Data . . . . . . . . . . . . . . . . . . . . 79Sashelp.leutrain — Leukemia Data Set - Training Data . . . . . . . . . . . . . . . . . . . . 79Sashelp.ltheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80Sashelp.macrs10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81Sashelp.macrs15 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82Sashelp.macrs20 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83Sashelp.macrs3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84Sashelp.macrs5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85Sashelp.macrs7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86Sashelp.margarin — Margarine Purchase Data . . . . . . . . . . . . . . . . . . . . . . . . . 87Sashelp.mdv — Sales Data and Forecast . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88Sashelp.mileages — Flying Mileages Between Ten US Cities . . . . . . . . . . . . . . . . . 90Sashelp.mon1001 — M-Competition 1001 Series, Monthly . . . . . . . . . . . . . . . . . . 91Sashelp.mon111 — M-Competition 111 Series, Monthly . . . . . . . . . . . . . . . . . . . 106Sashelp.mwelect — Midwest Electrical Supply Monthly Sales By Productgroup . . . . . . . 109Sashelp.nvst1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110Sashelp.nvst2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111Sashelp.nvst3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112Sashelp.nvst4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113Sashelp.nvst5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114Sashelp.orsales — Orion Star Sports & Outdoors Sales 1999 - 2002 . . . . . . . . . . . . . 115Sashelp.plfips — FIPS Place Codes from USGS Geographic Names Information System

(GNIS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117Sashelp.port_multi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119Sashelp.prdsal2 — Furniture Sales Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120Sashelp.prdsal3 — Furniture Sales Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122Sashelp.prdsale — Furniture Sales Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124Sashelp.pricedata — Simulated Monthly Sales Data With Hierarchy of Region, Line, Product 126Sashelp.proj4def . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

Sashelp.aacomp F 3

Sashelp.qtr1001 — M-Competition 1001 Series, Quarterly . . . . . . . . . . . . . . . . . . 131Sashelp.qtr111 — M-Competition 111 Series, Quarterly . . . . . . . . . . . . . . . . . . . 135Sashelp.rent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136Sashelp.retail — Retail Sales (Quarterly: 1980q1-1994q2) . . . . . . . . . . . . . . . . . . 137Sashelp.revhub2 — Airline Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138Sashelp.rockpit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140Sashelp.shoes — Fictitious Shoe Company Data . . . . . . . . . . . . . . . . . . . . . . . 141Sashelp.snacks — Daily Snack Food Sales . . . . . . . . . . . . . . . . . . . . . . . . . . . 143Sashelp.span_multi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145Sashelp.steel — Iron/Steel Exports (Yearly: 1937-1980) . . . . . . . . . . . . . . . . . . . 146Sashelp.stocks — Performance of Three Stocks from 1996 to 2005 . . . . . . . . . . . . . . 147Sashelp.syr1001 — M-Competition 1001 Series, Semiannual . . . . . . . . . . . . . . . . . 148Sashelp.tgrmapc — US County Names and State/County FIPS Codes . . . . . . . . . . . . 149Sashelp.tgrmaps — US State Names and FIPS Codes . . . . . . . . . . . . . . . . . . . . . 150Sashelp.thick — Coal Seam Thickness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151Sashelp.timedata — Time-Stamped Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152Sashelp.tourism — Tourism Demand Modelling and Forecasting . . . . . . . . . . . . . . . 153Sashelp.usecon — Source: US BEA, "Business Statistics" . . . . . . . . . . . . . . . . . . 154Sashelp.us_data — Apportionment, Population Change, Population Density . . . . . . . . . 155Sashelp.vbplayrs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160Sashelp.workers — Employment (Monthly: Jan77-Jul82) . . . . . . . . . . . . . . . . . . . 162Sashelp.yr1001 — M-Competition 1001 Series, Annual . . . . . . . . . . . . . . . . . . . . 163Sashelp.yr111 — M-Competition 111 Series, Annual . . . . . . . . . . . . . . . . . . . . . 181Sashelp.zh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184Sashelp.zipcode — US Zipcodes; Source: Zipcodedownload.com Jul 2013, SAS Release 9.4 185Sashelp.zipmil — US Military Zipcodes-Lat/LongNA Assigned Missing . . . . . . . . . . . 187Sashelp.zt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190

Sashelp.aacompThe Sashelp.aacomp data set provides advanced analytics model variable labels and error text. The followingsteps display information about the data set Sashelp.aacomp and create Figure 1.1. The data set contains1,530 observations.

title "Sashelp.aacomp";proc contents data=sashelp.aacomp varnum;

ods select position;run;

title "The First Five Observations Out of 1530";proc print data=sashelp.aacomp(obs=5) heading=h noobs;run;


Figure 1.1 Sashelp.aacomp

Sashelp.aacomp

The CONTENTS Procedure

Variables in Creation Order

# Variable Type Len

1 locale Char 52 key Char 603 lineno Num 44 text Char 1200

The First Five Observations Out of 1530

locale key lineno

en MODEL_ASE_VLABEL 1en MODEL_CODEDEPENDENCY_ERROR 1en MODEL_CORRECTRATE_VLABEL 1en MODEL_CRCUT_VLABEL 1en MODEL_CRDEPTH_VLABEL 1

text

Average Squared ErrorUnable to determine the code variable dependencies.Overall Precision RateCR CutoffCR Sample Depth

Sashelp.aarfm F 5

Sashelp.aarfmThe following steps display information about the data set Sashelp.aarfm and create Figure 1.2. The data setcontains 56 observations.

title "Sashelp.aarfm";proc contents data=sashelp.aarfm varnum;


title "The First Five Observations Out of 56";proc print data=sashelp.aarfm(obs=5) heading=h noobs;run;

Figure 1.2 Sashelp.aarfm

Sashelp.aarfm



# Variable Type Len

1 locale Char 52 key Char 603 lineno Num 44 text Char 1200


locale key lineno

en AVERAGE_PLURAL 1en AVERAGE_SINGULAR 1en CUSTOMERIDLABEL 1en CUSTOMER_ID_VARIABLE_NOT_FOUND 1en DIST_TITLE_TOTPURCHASEAMT_BY_RF 1

text

AveragesAverageCustomer Identifier%1z (%#1s) Required customer identifier variable is not specified.Distribution of %#1s of Transaction Amounts by Recency Score and Frequency Score


Sashelp.air — Airline Data (Monthly: Jan49-Dec60)The Sashelp.air data set provides airline data (monthly: Jan49–Dec60). The following steps displayinformation about the data set Sashelp.air and create Figure 1.3. The data set contains 144 observations.

title "Sashelp.air --- Airline Data (Monthly: Jan49-Dec60)";proc contents data=sashelp.air varnum;


title "The First Five Observations Out of 144";proc print data=sashelp.air(obs=5) heading=h noobs;run;

Figure 1.3 Sashelp.air — Airline Data (Monthly: Jan49-Dec60)

Sashelp.air --- Airline Data (Monthly: Jan49-Dec60)



# Variable Type Len Format Label

1 DATE Num 8 MONYY.2 AIR Num 8 international airline travel (thousands)


DATE AIR

JAN49 112FEB49 118MAR49 132APR49 129MAY49 121

Sashelp.applianc — Sales Time Series for 24 Appliances By Cycle F 7

Sashelp.applianc — Sales Time Series for 24 Appliances ByCycleThe Sashelp.applianc data set provides sales time series for 24 appliances by cycle. The following stepsdisplay information about the data set Sashelp.applianc and create Figure 1.4. The data set contains 156observations.

title "Sashelp.applianc --- Sales Time Series for 24 Appliances By Cycle";proc contents data=sashelp.applianc varnum;


title "The First Five Observations Out of 156";proc print data=sashelp.applianc(obs=5) heading=h noobs;run;

Figure 1.4 Sashelp.applianc — Sales Time Series for 24 Appliances By Cycle

Sashelp.applianc --- Sales Time Series for 24 Appliances By Cycle



# Variable Type Len Label

1 units_1 Num 8 units, appliance 12 units_2 Num 8 units, appliance 23 units_3 Num 8 units, appliance 34 units_4 Num 8 units, appliance 45 units_5 Num 8 units, appliance 56 units_6 Num 8 units, appliance 67 units_7 Num 8 units, appliance 78 units_8 Num 8 units, appliance 89 units_9 Num 8 units, appliance 9

10 units_10 Num 8 units, appliance 1011 units_11 Num 8 units, appliance 1112 units_12 Num 8 units, appliance 1213 units_13 Num 8 units, appliance 1314 units_14 Num 8 units, appliance 1415 units_15 Num 8 units, appliance 1516 units_16 Num 8 units, appliance 1617 units_17 Num 8 units, appliance 1718 units_18 Num 8 units, appliance 1819 units_19 Num 8 units, appliance 1920 units_20 Num 8 units, appliance 2021 units_21 Num 8 units, appliance 2122 units_22 Num 8 units, appliance 2223 units_23 Num 8 units, appliance 2324 units_24 Num 8 units, appliance 2425 cycle Num 8


Figure 1.4 continued


units_1 units_2 units_3 units_4 units_5 units_6 units_7 units_8 units_9

1 14 1 1 2 1 1 7 10 14 0 1 0 1 1 6 10 19 0 3 6 1 1 7 11 22 1 7 4 1 5 7 11 33 1 6 4 3 5 5 1

units_10 units_11 units_12 units_13 units_14 units_15 units_16 units_17

33 20 1 8 1 1 1 132 20 1 8 0 0 2 142 27 1 10 0 1 3 151 32 5 12 1 1 9 580 49 5 19 1 1 9 5

units_18 units_19 units_20 units_21 units_22 units_23 units_24 cycle

1 1 1 1 1 1 1 11 1 1 1 1 0 1 23 1 1 1 0 0 1 37 1 1 1 0 1 1 47 1 1 1 1 1 1 5

Sashelp.assocwa — Current Association List F 9

Sashelp.assocwa — Current Association ListThe Sashelp.assocwa data set provides an association list. The following steps display information about thedata set Sashelp.assocwa and create Figure 1.5. The data set contains 17,459 observations.

title "Sashelp.assocwa --- Current Association List";proc contents data=sashelp.assocwa varnum;


title "The First Five Observations Out of 17459";proc print data=sashelp.assocwa(obs=5) heading=h noobs;run;

Figure 1.5 Sashelp.assocwa — Current Association List

Sashelp.assocwa --- Current Association List



# Variable Type Len Format Informat Label

1 DELPRED Num 8 1. Delete Predecessor when delete successor2 DELSUCC Num 8 1. Delete Successor when delete predecessor3 PREDCLAS Char 20 $20. Predecessor Object Class4 PREDROLE Char 40 $40. Predecessor Role Name5 SUCCCLAS Char 20 $20. Successor Object Class6 SUCCROLE Char 40 $40. Successor Role Name7 DELPREDC Num 8 1. Delete Predecessor when cascade delete8 DELSUCCC Num 8 1. Delete Successor when cascading delete9 VERSION Num 8 Version number of association

10 _LOADTM Num 8 DATETIME20. DATETIME20. DateTime Stamp of when row was loaded




DELPRED DELSUCC PREDCLAS PREDROLE

0 1 COLUMN ANALYSIS0 1 CURRCOL ANALYSIS0 1 DATACOL ANALYSIS0 1 OLAPCOL ANALYSIS0 1 OLTPCOL ANALYSIS

SUCCCLAS SUCCROLE

SUMMCOL STATSUMMCOL STATSUMMCOL STATSUMMCOL STATSUMMCOL STAT

DELPREDC DELSUCCC VERSION _LOADTM

0 1 1.1 03FEB2000:11:22:000 1 1.1 03FEB2000:11:22:000 1 1.1 03FEB2000:11:22:000 1 1.1 03FEB2000:11:22:000 1 1.1 03FEB2000:11:22:00

Sashelp.baseball — 1986 Baseball Data F 11

Sashelp.baseball — 1986 Baseball DataThe Sashelp.Baseball data set contains salary and performance information for Major League Baseballplayers (excluding pitchers) who played at least one game in both the 1986 and 1987 seasons. The salariesare for the 1987 season, and the performance measures are from the 1986 season. The following stepsdisplay information about the data set Sashelp.baseball and create Figure 1.6. The data set contains 322observations.

title "Sashelp.baseball --- 1986 Baseball Data";proc contents data=sashelp.baseball varnum;


title "The First Five Observations Out of 322";proc print data=sashelp.baseball(obs=5) heading=h noobs;run;

Figure 1.6 Sashelp.baseball — 1986 Baseball Data

Sashelp.baseball --- 1986 Baseball Data




1 Name Char 18 Player's Name2 Team Char 14 Team at the End of 19863 nAtBat Num 8 Times at Bat in 19864 nHits Num 8 Hits in 19865 nHome Num 8 Home Runs in 19866 nRuns Num 8 Runs in 19867 nRBI Num 8 RBIs in 19868 nBB Num 8 Walks in 19869 YrMajor Num 8 Years in the Major Leagues

10 CrAtBat Num 8 Career Times at Bat11 CrHits Num 8 Career Hits12 CrHome Num 8 Career Home Runs13 CrRuns Num 8 Career Runs14 CrRbi Num 8 Career RBIs15 CrBB Num 8 Career Walks16 League Char 8 League at the End of 198617 Division Char 8 Division at the End of 198618 Position Char 8 Position(s) in 198619 nOuts Num 8 Put Outs in 198620 nAssts Num 8 Assists in 198621 nError Num 8 Errors in 198622 Salary Num 8 1987 Salary in $ Thousands23 Div Char 16 League and Division24 logSalary Num 8 Log Salary




n CrAt n n n Yr At Cr Cr

Name Team Bat Hits Home Runs nRBI nBB Major Bat Hits Home

Allanson, Andy Cleveland 293 66 1 30 29 14 1 293 66 1Ashby, Alan Houston 315 81 7 24 38 39 14 3449 835 69Davis, Alan Seattle 479 130 18 66 72 76 3 1624 457 63Dawson, Andre Montreal 496 141 20 65 78 37 11 5628 1575 225Galarraga, Andres Montreal 321 87 10 39 42 30 2 396 101 12

Cr Cr n n n logRuns Rbi CrBB League Division Position Outs Assts Error Salary Div Salary

30 29 14 American East C 446 33 20 . AE .321 414 375 National West C 632 43 10 475.0 NW 6.16331224 266 263 American West 1B 880 82 14 480.0 AW 6.17379828 838 354 National East RF 200 11 3 500.0 NE 6.2146148 46 33 National East 1B 805 40 4 91.5 NE 4.51634

Sashelp.bmt — Bone Marrow Transplant Patients F 13

Sashelp.bmt — Bone Marrow Transplant PatientsThe Sashelp.BMT (bone marrow transplant) data set is used to illustrate survival analysis methods. At thetime of transplant, each patient is classified into one of three risk categories: ALL (acute lymphoblasticleukemia), AML-Low Risk (acute myelocytic leukemia, low risk), and AML-High Risk. The endpointof interest is the disease-free survival time, which is the time in days to death, relapse, or the end of thestudy. In this data set, the variable Group represents the patient’s risk category, the variable T represents thedisease-free survival time, and the variable Status is the censoring indicator such that the value 1 indicates anevent time and the value 0 indicates a censored time. The following steps display information about the dataset Sashelp.bmt and create Figure 1.7. The data set contains 137 observations.

title "Sashelp.bmt --- Bone Marrow Transplant Patients";proc contents data=sashelp.bmt varnum;


title "The First Five Observations Out of 137";proc print data=sashelp.bmt(obs=5) heading=h noobs;run;

title "The Group Variable";proc freq data=sashelp.bmt;

tables Group;run;

Figure 1.7 Sashelp.bmt — Bone Marrow Transplant Patients

Sashelp.bmt --- Bone Marrow Transplant Patients




1 Group Char 13 Disease Group2 T Num 8 Disease-Free Survival Time3 Status Num 8 Event Indictor: 1=Event 0=Censored


Group T Status

ALL 2081 0ALL 1602 0ALL 1496 0ALL 1462 0ALL 1433 0



The Group Variable

The FREQ Procedure

Disease Group

Cumulative CumulativeGroup Frequency Percent Frequency Percent------------------------------------------------------------------ALL 38 27.74 38 27.74AML-High Risk 45 32.85 83 60.58AML-Low Risk 54 39.42 137 100.00

Sashelp.buy F 15

Sashelp.buyThe Sashelp.buy data set provides yearly date and amount for purchases. The following steps displayinformation about the data set Sashelp.buy and create Figure 1.8. The data set contains 11 observations.

title "Sashelp.buy";proc contents data=sashelp.buy varnum;


title "The First Five Observations Out of 11";proc print data=sashelp.buy(obs=5) heading=h noobs;run;

Figure 1.8 Sashelp.buy

Sashelp.buy




1 DATE Num 8 DATE9. Date2 AMOUNT Num 8


DATE AMOUNT

01JAN1996 -11000001JAN1997 -100001JAN1998 -100001JAN1999 -5100001JAN2000 -2000


Sashelp.bweight — Infant Birth WeightThe Sashelp.BWeight data set provides 1997 birth weight data from National Center for Health Statistics.The data record live, singleton births to mothers between the ages of 18 and 45 in the United States who wereclassified as black or white. The following steps display information about the data set Sashelp.bweight andcreate Figure 1.9. The data set contains 50,000 observations.

title "Sashelp.bweight --- Infant Birth Weight";proc contents data=sashelp.bweight varnum;


title "The First Five Observations Out of 50000";proc print data=sashelp.bweight(obs=5) heading=h noobs;run;

Figure 1.9 Sashelp.bweight — Infant Birth Weight

Sashelp.bweight --- Infant Birth Weight




1 Weight Num 8 Infant Birth Weight2 Black Num 8 Black Mother3 Married Num 8 Married Mother4 Boy Num 8 Baby Boy5 MomAge Num 8 Mother's Age6 MomSmoke Num 8 Smoking Mother7 CigsPerDay Num 8 Cigarettes Per Day8 MomWtGain Num 8 Mother's Pregnancy Weight Gain9 Visit Num 8 Prenatal Visit

10 MomEdLevel Num 8 Mother's Education Level


Mom MomMom Mom Cigs Wt Ed

Weight Black Married Boy Age Smoke PerDay Gain Visit Level

4111 0 1 1 -3 0 0 -16 1 03997 0 1 0 1 0 0 2 3 23572 0 1 1 0 0 0 -3 3 01956 0 1 1 -1 0 0 -5 3 23515 0 1 1 -6 0 0 -20 3 0

Sashelp.cars — 2004 Car Data F 17

Sashelp.cars — 2004 Car DataThe Sashelp.cars data set provides the 2004 car data. The following steps display information about the dataset Sashelp.cars and create Figure 1.10. The data set contains 428 observations.

title "Sashelp.cars --- 2004 Car Data";proc contents data=sashelp.cars varnum;


title "The First Five Observations Out of 428";proc print data=sashelp.cars(obs=5) heading=h noobs;run;

title "The Type Variable";proc freq data=sashelp.cars;

tables Type;run;

Figure 1.10 Sashelp.cars — 2004 Car Data

Sashelp.cars --- 2004 Car Data




1 Make Char 132 Model Char 403 Type Char 84 Origin Char 65 DriveTrain Char 56 MSRP Num 8 DOLLAR8.7 Invoice Num 8 DOLLAR8.8 EngineSize Num 8 Engine Size (L)9 Cylinders Num 8

10 Horsepower Num 811 MPG_City Num 8 MPG (City)12 MPG_Highway Num 8 MPG (Highway)13 Weight Num 8 Weight (LBS)14 Wheelbase Num 8 Wheelbase (IN)15 Length Num 8 Length (IN)




Drive EngineMake Model Type Origin Train MSRP Invoice Size

Acura MDX SUV Asia All $36,945 $33,337 3.5Acura RSX Type S 2dr Sedan Asia Front $23,820 $21,761 2.0Acura TSX 4dr Sedan Asia Front $26,990 $24,647 2.4Acura TL 4dr Sedan Asia Front $33,195 $30,299 3.2Acura 3.5 RL 4dr Sedan Asia Front $43,755 $39,014 3.5

MPG_Cylinders Horsepower MPG_City Highway Weight Wheelbase Length

6 265 17 23 4451 106 1894 200 24 31 2778 101 1724 200 22 29 3230 105 1836 270 20 28 3575 108 1866 225 18 24 3880 115 197

The Type Variable

The FREQ Procedure

Cumulative CumulativeType Frequency Percent Frequency Percent-----------------------------------------------------------Hybrid 3 0.70 3 0.70SUV 60 14.02 63 14.72Sedan 262 61.21 325 75.93Sports 49 11.45 374 87.38Truck 24 5.61 398 92.99Wagon 30 7.01 428 100.00

Sashelp.citiday — Citibase Daily Indicators: Jan88-Feb92 F 19

Sashelp.citiday — Citibase Daily Indicators: Jan88-Feb92The Sashelp.citiday data set provides Citibase daily indicators: Jan88–Feb92. The following steps dis-play information about the data set Sashelp.citiday and create Figure 1.11. The data set contains 1,069observations.

title "Sashelp.citiday --- Citibase Daily Indicators: Jan88-Feb92";proc contents data=sashelp.citiday varnum;


title "The First Five Observations Out of 1069";proc print data=sashelp.citiday(obs=5) heading=h noobs;run;

Figure 1.11 Sashelp.citiday — Citibase Daily Indicators: Jan88-Feb92

Sashelp.citiday --- Citibase Daily Indicators: Jan88-Feb92




1 DATE Num 7 DATE9. Date of Observation2 SNYDJCM Num 8 STOCK MKT INDEX:NY DOW JONES COMPOSITE,3 SNYSECM Num 8 STOCK MKT INDEX:NYSE COMPOSITE, (WSJ)4 DSIUSWIL Num 8 STOCK MKT INDEX:WILSHIRE 500, (WSJ)5 DFXWCAN Num 8 FOREIGN EXCH RATE WSJ:CANADA,CANADIAN $/6 DFXWUK90 Num 8 FOREIGN EXCH RATE WSJ:U.K.,CENTS/POUND(97 DSIUKAS Num 8 STOCK MKT INDEX:U.K. - ALL SHARES8 DSIJPND Num 8 STOCK MKT INDEX:JAPAN - NIKKEI-DOW9 DCP07 Num 8 7 DAY COMMERCIAL PAPER RATE, SHORT-TERM

10 DCD1M Num 8 INT.RATE:1MO CERTIFICATES OF DEPOSIT, SH11 DTBD3M Num 8 INT.RATE:3MO T-BILL, DISCOUNT YIELD (FRB


DATE SNYDJCM SNYSECM DSIUSWIL DFXWCAN DFXWUK90 DSIUKAS DSIJPND DCP07 DCD1M DTBD3M

01JAN1988 . . . 1.29630 186.950 886.500 21217.04 . . .04JAN1988 740.200 142.900 2494.93 . . 886.500 21217.04 . 6.89000 5.8800005JAN1988 747.380 144.540 2526.99 . . 908.210 21575.28 . 6.85000 5.9300006JAN1988 750.400 144.820 2534.49 1.28920 180.250 908.210 22790.50 . 6.87000 5.8400007JAN1988 757.040 145.890 2553.39 1.28700 179.930 908.490 22792.13 . 6.88000 5.78000


Sashelp.citimon — Citibase Monthly Indicators: Jan80-Jan92The Sashelp.citimon data set provides Citibase monthly indicators: Jan80–Jan92. The following steps displayinformation about the data set Sashelp.citimon and create Figure 1.12. The data set contains 145 observations.

title "Sashelp.citimon --- Citibase Monthly Indicators: Jan80-Jan92";proc contents data=sashelp.citimon varnum;


title "The First Five Observations Out of 145";proc print data=sashelp.citimon(obs=5) heading=h noobs;run;

Figure 1.12 Sashelp.citimon — Citibase Monthly Indicators: Jan80-Jan92

Sashelp.citimon --- Citibase Monthly Indicators: Jan80-Jan92




1 DATE Num 7 MONYY7. Date of Observation2 CCIUAC Num 8 CONSUMER INSTAL CR OUTST'G: AUTOMOBILE,C3 CCIUTC Num 8 CONSUMER INSTAL CR OUTST'G: TOTAL, COM'L4 CONB Num 8 CONSTRUCT.PUT IN PLACE: COMM'L & INDUSTR5 CONQ Num 8 CONSTRUCT.PUT IN PLACE: TOTAL PUBLIC, (M6 EEC Num 8 ENERGY CONSUM:TOTAL(QUADRILLION BTU)7 EEGP Num 8 GASOLINE:RETAIL PRICE, ALL TYPES (CTS/GA8 EXVUS Num 8 WEIGHTED-AVERAGE EXCHANGE VALUE OF U.S.D9 FM1 Num 8 MONEY STOCK: M1(CURR,TRAV.CKS,DEM DEP,OT

10 FM1D82 Num 8 MONEY STOCK: M-1 IN 1982$ (BIL$,SA)(BCD11 FSPCAP Num 8 S&P'S COMMON STOCK PRICE INDEX: CAPITAL12 FSPCOM Num 8 S&P'S COMMON STOCK PRICE INDEX: COMPOSIT13 FSPCON Num 8 S&P'S COMMON STOCK PRICE INDEX: CONSUMER14 IP Num 8 INDUSTRIAL PRODUCTION: TOTAL INDEX (198715 LHUR Num 8 UNEMPLOYMENT RATE: ALL WORKERS, 16 YEARS16 LUINC Num 8 AVG WKLY INITIAL CLAIMS,STATE UNEMPLOY.I17 PW Num 8 PRODUCER PRICE INDEX: ALL COMMODITIES (818 RCARD Num 8 RETAIL SALES: NEW PASSENGER CARS, DOMEST19 RTRR Num 8 RETAIL SALES: TOTAL (MIL$,SA)

Sashelp.citimon — Citibase Monthly Indicators: Jan80-Jan92 F 21



DATE CCIUAC CCIUTC CONB CONQ EEC EEGP EXVUS FM1 FM1D82

JAN1980 67166 153636 48579 66820 7.40300 111.000 85.5200 386.100 477.800FEB1980 67119 153308 47759 64049 6.96200 118.600 86.3700 389.800 476.500MAR1980 66786 152347 46705 64831 6.84800 123.000 90.2600 389.300 468.500APR1980 65837 150937 45835 63913 5.98600 124.200 91.0900 383.700 457.300MAY1980 65035 149238 46819 64598 5.83700 124.400 86.9600 383.200 452.400

FSPCAP FSPCOM FSPCON IP LHUR LUINC PW RCARD RTRR

126.680 110.870 85.0900 85.9000 6.30000 416 85.2000 8.34000 79407131.270 115.340 83.1400 86.2000 6.30000 397 86.9000 7.86000 78787116.200 104.690 75.5000 86.2000 6.30000 438 87.5000 7.14000 77685110.200 102.970 76.9300 84.5000 6.90000 532 87.8000 5.92000 76658113.460 107.690 82.8100 82.5000 7.50000 616 88.3000 5.32000 76613


Sashelp.citiqtr — Citibase Quarterly Indicators: 80: 1-91: 4The Sashelp.citiqtr data set provides Citibase quarterly indicators: Jan80–Apr91. The following steps displayinformation about the data set Sashelp.citiqtr and create Figure 1.13. The data set contains 48 observations.

title "Sashelp.citiqtr --- Citibase Quarterly Indicators: 80: 1-91: 4";proc contents data=sashelp.citiqtr varnum;


title "The First Five Observations Out of 48";proc print data=sashelp.citiqtr(obs=5) heading=h noobs;run;

Figure 1.13 Sashelp.citiqtr — Citibase Quarterly Indicators: 80: 1-91: 4

Sashelp.citiqtr --- Citibase Quarterly Indicators: 80: 1-91: 4




1 DATE Num 7 YYQC6. Date of Observation2 BPB Num 8 BAL OF P'MENT:BALANCE ON MERCHANDISE TRA3 BPCR Num 8 BAL OF P'MENT:BAL ON CURRENT A/C(INC REI4 GC Num 8 PERSONAL CONSUMPTION EXPENDITURES5 GCQ Num 8 PERSONAL CONSUMPTION EXPENDITURES (BIL.6 GCD Num 8 PERSONAL CONS. EXPENDITURES, DURABLE GOO7 GCDQ Num 8 PERSONAL CONSUMPTION EXPENDITURES:DUR GO8 GD Num 8 IMPLICIT PR DEFLATOR: GROSS NATIONAL PRO9 GDP Num 8 GROSS DOMESTIC PRODUCT (BIL.$,SAAR)(T.1.

10 GDPQ Num 8 GROSS DOMESTIC PRODUCT (BIL. 1987$)(T.1.11 GNP Num 8 GROSS NATIONAL PRODUCT, TOTAL12 GNPQ Num 8 GROSS NATIONAL PRODUCT (BILL.1987$)(T1.113 GY Num 8 NATIONAL INCOME, TOTAL14 GYD Num 8 PERSN'L INCOME: DISPOSABLE PERSONAL INCO15 GYDQ Num 8 DISPOSABLE PERSONAL INCOME: TOTAL (BIL.8

Sashelp.citiqtr — Citibase Quarterly Indicators: 80: 1-91: 4 F 23



DATE BPB BPCR GC GCQ GCD GCDQ GD

1980:1 -10575 -2785 1701.50 2464.60 218.700 279.700 69.20001980:2 -6253 -1197 1704.90 2414.20 198.200 246.300 70.80001980:3 -3856 3396 1762.30 2440.30 211.300 258.400 72.50001980:4 -4797 1704 1823.60 2469.20 221.800 266.600 74.40001981:1 -5663 2450 1876.00 2475.50 230.800 274.400 76.5000

GDP GDPQ GNP GNPQ GY GYD GYDQ

2650.10 3830.80 2687.70 3884.60 2163.40 1893.70 2742.902643.90 3732.60 2679.40 3782.30 2136.80 1901.10 2692.002705.30 3733.50 2739.80 3780.50 2189.70 1966.10 2722.502832.90 3808.50 2861.50 3846.20 2302.90 2050.90 2777.002953.50 3860.50 2985.50 3901.60 2375.70 2106.50 2779.70


Sashelp.citiwk — Citibase Weekly Indicators: Dec85-Jan92The Sashelp.citiwk data set provides Citibase weekly indicators: Dec85–Jan92. The following steps displayinformation about the data set Sashelp.citiwk and create Figure 1.14. The data set contains 319 observations.

title "Sashelp.citiwk --- Citibase Weekly Indicators: Dec85-Jan92";proc contents data=sashelp.citiwk varnum;


title "The First Five Observations Out of 319";proc print data=sashelp.citiwk(obs=5) heading=h noobs;run;

Figure 1.14 Sashelp.citiwk — Citibase Weekly Indicators: Dec85-Jan92

Sashelp.citiwk --- Citibase Weekly Indicators: Dec85-Jan92




1 DATE Num 7 WEEKDATX16. Date of Observation2 MF3505 Num 8 MONEY STOCK:M1(CURRENCY+DEMAND DEP+OTHER3 TCJ Num 8 INDUSTRIAL MATERIALS PRICE INDEX, 18 COM4 WSPCA Num 8 STANDARD & POOR'S WEEKLY BOND YIELD: COM5 WSPUA Num 8 STANDARD & POOR'S WEEKLY BOND YIELD: UTI6 WSPIA Num 8 STANDARD & POOR'S WEEKLY BOND YIELD:INDU7 WSPGLT Num 8 STANDARD & POOR;S WEEKLY BOND YIELD: GOV8 HFBI20 Num 8 BOND BUYERS INDEX: 20 BOND GENERAL OBLIG9 FF142B Num 8 BOND YIELD:"A" UTILITY(RECENTLY OFFERED)

10 FCPOIL Num 8 PETROLEUM, REFINED OIL PRICES: FUEL OIL,


DATE MF3505 TCJ WSPCA WSPUA WSPIA WSPGLT HFBI20 FF142B FCPOIL

Sun, 22 Dec 85 . . 10.4340 10.5380 10.3290 9.24000 8.33000 10.5900 0.76750Sun, 29 Dec 85 620.800 . 10.4900 10.4200 10.5600 9.32000 8.04000 10.8300 0.73250Sun, 5 Jan 86 620.900 . 10.5700 10.6900 10.4500 9.51000 8.10000 10.7500 0.64000Sun, 12 Jan 86 620.500 . 10.5800 10.5800 10.5800 9.50000 8.05000 10.8200 0.58250Sun, 19 Jan 86 622.200 . 10.2600 10.4200 10.1000 9.30000 7.86000 10.6700 0.57250

Sashelp.citiyr — Citibase New File Format F 25

Sashelp.citiyr — Citibase New File FormatThe Sashelp.citiyr data set provides Citibase new file format. The following steps display information aboutthe data set Sashelp.citiyr and create Figure 1.15. The data set contains 10 observations.

title "Sashelp.citiyr --- Citibase New File Format";proc contents data=sashelp.citiyr varnum;


title "The First Five Observations Out of 10";proc print data=sashelp.citiyr(obs=5) heading=h noobs;run;

Figure 1.15 Sashelp.citiyr — Citibase New File Format

Sashelp.citiyr --- Citibase New File Format




1 DATE Num 6 YEAR4. Date of Observation2 PAN Num 7 POPULATION EST.: ALL AGES, INC.ARMED F.3 PAN17 Num 7 POPULATION EST.: 16 YRS AND OVER,INC ARM4 PAN18 Num 7 POPULATION EST.: 18-64 YRS,INC.ARMED F.O5 PANF Num 7 POPULATION EST.: FEMALES,ALL AGES,INC.AR6 PANM Num 7 POPULATION EST.: MALES, ALL AGES, INC.AR


DATE PAN PAN17 PAN18 PANF PANM

1980 227757 172456 138358 116869 1108881981 230138 175017 140618 118074 1120641982 232520 177346 142740 119275 1132451983 234799 179480 144591 120414 1143851984 237001 181514 146257 121507 115494


Sashelp.class — Student DataThe Sashelp.Class data set provides information about a small fictitious class of students. Variables includeSex, Age, Height, and Weight. This data set is frequently used in SAS documentation to illustrate basic SAScoding. The following steps display information about the data set Sashelp.class and create Figure 1.16. Thedata set contains 19 observations.

title "Sashelp.class --- Student Data";proc contents data=sashelp.class varnum;


title "The First Five Observations Out of 19";proc print data=sashelp.class(obs=5) heading=h noobs;run;

Figure 1.16 Sashelp.class — Student Data

Sashelp.class --- Student Data



# Variable Type Len

1 Name Char 82 Sex Char 13 Age Num 84 Height Num 85 Weight Num 8


Name Sex Age Height Weight

Alfred M 14 69.0 112.5Alice F 13 56.5 84.0Barbara F 13 65.3 98.0Carol F 14 62.8 102.5Henry M 14 63.5 102.5

Sashelp.classfit — Predicted Weights With Confidence Limits F 27

Sashelp.classfit — Predicted Weights With Confidence LimitsThe Sashelp.classfit data set provides predicted weights with confidence limits. The following steps displayinformation about the data set Sashelp.classfit and create Figure 1.17. The data set contains 19 observations.

title "Sashelp.classfit --- Predicted Weights With Confidence Limits";proc contents data=sashelp.classfit varnum;


title "The First Five Observations Out of 19";proc print data=sashelp.classfit(obs=5) heading=h noobs;run;

Figure 1.17 Sashelp.classfit — Predicted Weights With Confidence Limits

Sashelp.classfit --- Predicted Weights With Confidence Limits




1 Name Char 82 Sex Char 13 Age Num 84 Height Num 85 Weight Num 86 predict Num 8 Predicted Value of Weight7 lowermean Num 8 Lower Bound of 95% C.I. for Mean8 uppermean Num 8 Upper Bound of 95% C.I. for Mean9 lower Num 8 Lower Bound of 95% C.I.(Individual Pred)

10 upper Num 8 Upper Bound of 95% C.I.(Individual Pred)


Name Sex Age Height Weight predict lowermean uppermean lower upper

Joyce F 11 51.3 50.5 56.9933 43.8044 70.1823 29.8835 84.103Louise F 12 56.3 77.0 76.4885 67.9601 85.0169 51.3145 101.662Alice F 13 56.5 84.0 77.2683 68.9066 85.6300 52.1503 102.386James M 12 57.3 83.0 80.3875 72.6671 88.1079 55.4757 105.299Thomas M 11 57.5 85.0 81.1673 73.6000 88.7346 56.3025 106.032


Sashelp.comet — Comet Assay DataThe Sashelp.Comet data set provides information from the following experiment. Twenty-four male rats weredivided into four groups. Three groups received a daily oral dose of a 1,2-dimethylhydrazine dihydrochloridein three dose levels (low, medium, and high, respectively); the fourth group was a control group. Threeadditional animals received a positive control. Cell suspensions for each animal were scored for DNA damageby using a comet assay. The following steps display information about the data set Sashelp.comet and createFigure 1.18. The data set contains 4,050 observations.

title "Sashelp.comet --- Comet Assay Data";proc contents data=sashelp.comet varnum;


title "The First Five Observations Out of 4050";proc print data=sashelp.comet(obs=5) heading=h noobs;run;

Figure 1.18 Sashelp.comet — Comet Assay Data

Sashelp.comet --- Comet Assay Data




1 Dose Num 8 1,2 Dimethylhydrazine dihydrochloride Dose Level2 Rat Num 8 Rat Index3 Sample Num 8 Slide Index of Grouped Cells from a Rat4 Length Num 8 Tail Length of the Comet


Dose Rat Sample Length

0 1 1 15.35270 1 1 16.18260 1 1 14.93780 1 1 12.44810 1 1 12.8631

Sashelp.company — Several Hierarchical Levels of a Company F 29

Sashelp.company — Several Hierarchical Levels of aCompanyThe Sashelp.company data set provides several hierarchical levels of a company. The following stepsdisplay information about the data set Sashelp.company and create Figure 1.19. The data set contains 48observations.

title "Sashelp.company --- Several Hierarchical Levels of a Company";proc contents data=sashelp.company varnum;


title "The First Five Observations Out of 48";proc print data=sashelp.company(obs=5) heading=h noobs;run;

Figure 1.19 Sashelp.company — Several Hierarchical Levels of a Company

Sashelp.company --- Several Hierarchical Levels of a Company



# Variable Type Len

1 LEVEL2 Char 132 LEVEL1 Char 163 LEVEL5 Char 304 DEPTHEAD Char 155 LEVEL3 Char 206 LEVEL4 Char 307 JOB1 Char 158 N Num 8


LEVEL2 LEVEL1 LEVEL5 DEPTHEAD LEVEL3 LEVEL4 JOB1 N

TOKYO International Ai So Suumi 1 ADMIN CONTRACTS MANAGER 1TOKYO International Ai Steffen Graff 2 ADMIN CONTRACTS ASSISTANT 1TOKYO International Ai Karin Schmidt 2 ADMIN FINANCE ACCOUNTANT 1LONDON International Ai Anne Bauer 1 ADMIN PERSONNEL MANAGER 1TOKYO International Ai Barbara Bial 2 ADMIN PERSONNEL ADMIN 1


Sashelp.demographics — Data Derived from World PopulationProspects: the 2004 RevisionThe Sashelp.demographics data set provides the 2004 revision of data derived from world populationprospects. The following steps display information about the data set Sashelp.demographics and createFigure 1.20. The data set contains 197 observations.

title "Sashelp.demographics --- Data Derived from World Population Prospects: the 2004"" Revision";

proc contents data=sashelp.demographics varnum;ods select position;

run;

title "The First Five Observations Out of 197";proc print data=sashelp.demographics(obs=5) heading=h noobs;run;

title "The region Variable";proc freq data=sashelp.demographics;

tables region;run;

Sashelp.demographics — Data Derived from World Population Prospects: the 2004 Revision F 31

Figure 1.20 Sashelp.demographics — Data Derived from World Population Prospects: the 2004 Revision

Sashelp.demographics --- Data Derived from World Population Prospects: the 2004 Revision




1 CONT Num 8 Numeric Rep. for Continent2 ID Num 8 GLC Country ID Number3 ISO Num 8 Z3. ISO Country Number: 900+ Undefined4 NAME Char 45 GLC Country Name5 ISONAME Char 45 ISO Name for Country6 region Char 6 Region7 pop Num 8 COMMA15. Population (2005)8 popAGR Num 8 PERCENTN9.2 Population Annual Growth Rate Percentage (1995-2005)9 popUrban Num 8 PERCENTN9.2 Population in Urban Areas Percentage (2005)

10 totalFR Num 8 Total Fertility Rate (per woman 2004)11 Adolescent Num 8 PERCENTN9.2 Adolescent Fertility Proportion Percentage

FPpct12 Adolescent Num 8 Adolescent Fertility Proportion Year

FPyear13 Adult Num 8 PERCENTN9.2 Adult Literacy Rate Percentage (2000-2004)

Literacypct14 MaleSchoolpct Num 8 PERCENTN9.2 Net Primary School Enrollment Ratio

- Male Percentage (1998-2004)15 Female Num 8 PERCENTN9.2 Net Primary School Enrollment Ratio

Schoolpct - Female Percentage (1998-2004)16 GNI Num 8 Gross National Income per Capita (PPP Int.$ 2004)17 PopPovertypct Num 8 PERCENTN9.2 Population Living Below the Poverty

Line (% with <$1 a day)18 PopPovertyYear Num 8 Population Living Below the Poverty Line (Year)


totalCONT ID ISO NAME ISONAME region pop popAGR popUrban FR

91 180 044 BAHAMAS BAHAMAS AMR 323,063 1.34% 90.00% 2.391 227 084 BELIZE BELIZE AMR 269,736 2.14% 48.60% 3.191 260 124 CANADA CANADA AMR 32,268,243 0.87% 81.10% 1.591 295 188 COSTA RICA COSTA RICA AMR 4,327,228 2.04% 61.70% 2.291 300 192 CUBA CUBA AMR 11,269,400 0.34% 76.00% 1.6

PopAdolescent Adolescent Adult Male Female Pop PovertyFPpct FPyear Literacypct Schoolpct Schoolpct GNI Povertypct Year

. . . 85.00% 88.00% 16140 . .12.50% 1998 76.90% 98.00% 100.00% 6510 . .6.50% 1997 . 100.00% 100.00% 30660 . .

17.40% 1999 95.80% 90.00% 91.00% 9530 2.00% 200016.00% 2000 99.80% 96.00% 95.00% . . .



The region Variable

The FREQ Procedure

Region

Cumulative Cumulativeregion Frequency Percent Frequency Percent-----------------------------------------------------------AFR 46 23.35 46 23.35AMR 35 17.77 81 41.12EMR 21 10.66 102 51.78EUR 55 27.92 157 79.70SEAR 11 5.58 168 85.28WPR 29 14.72 197 100.00

Sashelp.electric — Electric Power Generation and Revenue F 33

Sashelp.electric — Electric Power Generation and RevenueThe Sashelp.electric data set provides electric power generation and revenue. The following steps displayinformation about the data set Sashelp.electric and create Figure 1.21. The data set contains 48 observations.

title "Sashelp.electric --- Electric Power Generation and Revenue";proc contents data=sashelp.electric varnum;


title "The First Five Observations Out of 48";proc print data=sashelp.electric(obs=5) heading=h noobs;run;

title "The Customer Variable";proc freq data=sashelp.electric;

tables Customer;run;

Figure 1.21 Sashelp.electric — Electric Power Generation and Revenue

Sashelp.electric --- Electric Power Generation and Revenue




1 Customer Char 122 Revenue Num 8 DOLLAR10. Revenue ($B)3 Year Num 84 RevTip Char 755 AllPower Num 8 COMMA10. All6 AllTip Char 757 Coal Num 8 COMMA10.8 CoalTip Char 759 Nuclear Num 8 COMMA10.

10 NukeTip Char 7511 NaturalGas Num 8 COMMA10. Natural Gas12 GasTip Char 7513 Hydro Num 8 COMMA10. Hydropower14 HydroTip Char 7515 Other Num 816 OtherTip Char 75




Customer Revenue Year RevTip

Commercial $63 1994 title="Bar: CommercialRevenue: $63 (billion)"Industrial $48 1994 title="Bar: Industrial

Revenue: $48 (billion)"Other $7 1994 title="Bar: Other

Revenue: $7 (billion)"Residential $85 1994 title="Bar: Residential

Revenue: $85 (billion)"Commercial $66 1995 title="Bar: Commercial

Revenue: $66 (billion)"

AllPower AllTip Coal

. .

. .

. .3,247,522 title="Line: All sources

Year: 1994Power: 3,247,522 GWh" 1,690,694

. .

CoalTip Nuclear

.

.

.title="Line: Coal

Year: 1994Power: 1,690,694 GWh" 640,440

.

NukeTip NaturalGas

.

.

.title="Line: Nuclear

Year: 1994Power: 640,440 GWh" 460,219

.

GasTip Hydro

.

.

.title="Line: Natural Gas

Year: 1994Power: 460,219 GWh" 260,126

.

Sashelp.electric — Electric Power Generation and Revenue F 35



HydroTip Other

.

.

.title="Line: Hydropower

Year: 1994Power: 260,126 GWh" 196044

.

OtherTip

title="Line: Other sourcesYear: 1994Power: 196,044 GWh"

The Customer Variable

The FREQ Procedure

Cumulative CumulativeCustomer Frequency Percent Frequency Percent----------------------------------------------------------------Commercial 12 25.00 12 25.00Industrial 12 25.00 24 50.00Other 12 25.00 36 75.00Residential 12 25.00 48 100.00


Sashelp.eng_multiThe following steps display information about the data set Sashelp.eng_multi and create Figure 1.22. Thedata set contains 398 observations.

title "Sashelp.eng_multi";proc contents data=sashelp.eng_multi varnum;


title "The First Five Observations Out of 398";proc print data=sashelp.eng_multi(obs=5) heading=h noobs;run;

Figure 1.22 Sashelp.eng_multi

Sashelp.eng_multi




1 Term Char 256 $256. $256. Term2 Role Char 12 Role


Term

a bita fortioria lota lot ofa posteriori

Role

AdvAdvAdvAdjAdj

Sashelp.enso — El Nino Southern Oscillation F 37

Sashelp.enso — El Nino Southern OscillationThe Sashelp.ENSO (El Niño–Southern Oscillation) data set contains measurements of monthly averagedatmospheric pressure differences between Easter Island and Darwin, Australia, for a period of 168 months.These pressure differences drive the southern trade winds. This data set is used to illustrate fitting nonlinearfunctions to a scatter plot by using methods such as loess and penalized B-splines. These data show bothseasonal variations and variations due to El Niño. The following steps display information about the data setSashelp.enso and create Figure 1.23. The data set contains 168 observations.

title "Sashelp.enso --- El Nino Southern Oscillation";proc contents data=sashelp.enso varnum;


title "The First Five Observations Out of 168";proc print data=sashelp.enso(obs=5) heading=h noobs;run;

Figure 1.23 Sashelp.enso — El Nino Southern Oscillation

Sashelp.enso --- El Nino Southern Oscillation



# Variable Type Len

1 Month Num 82 Year Num 83 Pressure Num 8


Month Year Pressure

1 0.08333 12.92 0.16667 11.33 0.25000 10.64 0.33333 11.25 0.41667 10.9


Sashelp.failure — MOS Capacitor FailureThe Sashelp.failure data set provides MOS capacitor failure data. The following steps display informationabout the data set Sashelp.failure and create Figure 1.24. The data set contains 70 observations.

title "Sashelp.failure --- MOS Capacitor Failure";proc contents data=sashelp.failure varnum;


title "The First Five Observations Out of 70";proc print data=sashelp.failure(obs=5) heading=h noobs;run;

title "The Cause and Process Variables";proc freq data=sashelp.failure;

tables Cause;tables Process;

run;

Figure 1.24 Sashelp.failure — MOS Capacitor Failure

Sashelp.failure --- MOS Capacitor Failure




1 Cause Char 14 Cause of Failure2 Process Char 93 Count Num 84 Day Num 8 DOWNAME.


Cause Process Count Day

Contamination Process A 15 MondayCorrosion Process A 2 MondayDoping Process A 1 MondayMetallization Process A 2 MondayMiscellaneous Process A 3 Monday

Sashelp.failure — MOS Capacitor Failure F 39


The Cause and Process Variables

The FREQ Procedure

Cause of Failure

Cumulative CumulativeCause Frequency Percent Frequency Percent-------------------------------------------------------------------Contamination 10 14.29 10 14.29Corrosion 10 14.29 20 28.57Doping 10 14.29 30 42.86Metallization 10 14.29 40 57.14Miscellaneous 10 14.29 50 71.43Oxide Defect 10 14.29 60 85.71Silicon Defect 10 14.29 70 100.00

Cumulative CumulativeProcess Frequency Percent Frequency Percent--------------------------------------------------------------Process A 35 50.00 35 50.00Process B 35 50.00 70 100.00


Sashelp.fish — Measurements of 159 Fish Caught in LakeLaengelmavesi, FinlandThe Sashelp.Fish data set contains measurements of 159 fish that were caught in Finland’s Lake Laengel-maevesi; it is used to illustrate discriminant analysis. For each of the seven species (bream, roach, whitefish,parkki, perch, pike, and smelt), the weight, length, height, and width of each fish are tallied. Three differentlength measurements are recorded: from the nose of the fish to the beginning of its tail, from the nose to thenotch of its tail, and from the nose to the end of its tail. The height and width are recorded as percentages ofthe third length variable. The following steps display information about the data set Sashelp.fish and createFigure 1.25. The data set contains 159 observations.

title "Sashelp.fish --- Measurements of 159 Fish Caught in Lake Laengelmavesi, Finland";proc contents data=sashelp.fish varnum;


title "The First Five Observations Out of 159";proc print data=sashelp.fish(obs=5) heading=h noobs;run;

title "The Species Variable";proc freq data=sashelp.fish;

tables Species;run;

Figure 1.25 Sashelp.fish — Measurements of 159 Fish Caught in Lake Laengelmavesi, Finland

Sashelp.fish --- Measurements of 159 Fish Caught in Lake Laengelmavesi, Finland



# Variable Type Len

1 Species Char 92 Weight Num 83 Length1 Num 84 Length2 Num 85 Length3 Num 86 Height Num 87 Width Num 8

Sashelp.fish — Measurements of 159 Fish Caught in Lake Laengelmavesi, Finland F 41



Species Weight Length1 Length2 Length3 Height Width

Bream 242 23.2 25.4 30.0 11.5200 4.0200Bream 290 24.0 26.3 31.2 12.4800 4.3056Bream 340 23.9 26.5 31.1 12.3778 4.6961Bream 363 26.3 29.0 33.5 12.7300 4.4555Bream 430 26.5 29.0 34.0 12.4440 5.1340

The Species Variable

The FREQ Procedure

Cumulative CumulativeSpecies Frequency Percent Frequency Percent--------------------------------------------------------------Bream 35 22.01 35 22.01Parkki 11 6.92 46 28.93Perch 56 35.22 102 64.15Pike 17 10.69 119 74.84Roach 20 12.58 139 87.42Smelt 14 8.81 153 96.23Whitefish 6 3.77 159 100.00


Sashelp.frnch_multiThe following steps display information about the data set Sashelp.frnch_multi and create Figure 1.26. Thedata set contains 1,941 observations.

title "Sashelp.frnch_multi";proc contents data=sashelp.frnch_multi varnum;


Figure 1.26 Sashelp.frnch_multi

Sashelp.frnch_multi




1 Term Char 33 $33. $33. Term2 Role Char 4 $4. $4. Role

Sashelp.ftable F 43

Sashelp.ftableThe following steps display information about the data set Sashelp.ftable and create Figure 1.27. The dataset contains 133,050 observations.

title "Sashelp.ftable";proc contents data=sashelp.ftable varnum;


title "The First Five Observations Out of 133050";proc print data=sashelp.ftable(obs=5) heading=h noobs;run;

Figure 1.27 Sashelp.ftable

Sashelp.ftable



# Variable Type Len

1 FEANAME Char 30


FEANAME

StreamBurlington Northern RailroadConrail RailroadMainUnion Pacific Railroad


Sashelp.gas — Nitrogen Oxide Emissions from a SingleCylinder EngineThe Sashelp.Gas data set contains data from an experiment about gasoline engine exhaust emissions.Nitrogen oxide emissions from a single-cylinder engine are measured for various combinations of fuel,compression ratio, and equivalence ratio. This data set is used to illustrate fitting models with nonlinearlytransformed data. The following steps display information about the data set Sashelp.gas and createFigure 1.28. The data set contains 171 observations.

title "Sashelp.gas --- Nitrogen Oxide Emissions from a Single Cylinder Engine";proc contents data=sashelp.gas varnum;


title "The First Five Observations Out of 171";proc print data=sashelp.gas(obs=5) heading=h noobs;run;

title "The Fuel Variable";proc freq data=sashelp.gas;

tables Fuel;run;

Figure 1.28 Sashelp.gas — Nitrogen Oxide Emissions from a Single Cylinder Engine

Sashelp.gas --- Nitrogen Oxide Emissions from a Single Cylinder Engine




1 Fuel Char 82 CpRatio Num 8 Compression Ratio3 EqRatio Num 8 Equivalence Ratio4 NOx Num 8 Nitrogen Oxide


Cp EqFuel Ratio Ratio NOx

Ethanol 12 0.907 3.741Ethanol 12 0.761 2.295Ethanol 12 1.108 1.498Ethanol 12 1.016 2.881Ethanol 12 1.189 0.760

Sashelp.gas — Nitrogen Oxide Emissions from a Single Cylinder Engine F 45


The Fuel Variable

The FREQ Procedure

Cumulative CumulativeFuel Frequency Percent Frequency Percent-------------------------------------------------------------82rongas 9 5.26 9 5.2694%Eth 25 14.62 34 19.88Ethanol 90 52.63 124 72.51Gasohol 13 7.60 137 80.12Indolene 22 12.87 159 92.98Methanol 12 7.02 171 100.00


Sashelp.gcdirect — Standard Street Direction Abbreviationsfor US GeocodingThe Sashelp.gcdirect data set provides standard street direction abbreviations for geocoding, updated July2012. The following steps display information about the data set Sashelp.gcdirect and create Figure 1.29.The data set contains 16 observations.

title "Sashelp.gcdirect --- Standard Street Direction Abbreviations for US Geocoding";proc contents data=sashelp.gcdirect varnum;


title "The First Five Observations Out of 16";proc print data=sashelp.gcdirect(obs=5) heading=h noobs;run;

Figure 1.29 Sashelp.gcdirect — Standard Street Direction Abbreviations for US Geocoding

Sashelp.gcdirect --- Standard Street Direction Abbreviations for US Geocoding




1 DIRECTION Char 20 Street direction prefix/sufix or abbreviation2 DIRABRV Char 10 Street direction abbreviation


DIRECTION DIRABRV

E EEAST EN NNE NENORTH N

Sashelp.gcstate — State/Province Names and Abbreviation for Geocoding F 47

Sashelp.gcstate — State/Province Names and Abbreviationfor GeocodingThe Sashelp.gcstate data set provides state/province/region names and abbreviations for geocoding. Thefollowing steps display information about the data set Sashelp.gcstate and create Figure 1.30. The data setcontains 74 observations.

title "Sashelp.gcstate --- State/Province Names and Abbreviation for Geocoding";proc contents data=sashelp.gcstate varnum;


title "The First Five Observations Out of 74";proc print data=sashelp.gcstate(obs=5) heading=h noobs;run;

Figure 1.30 Sashelp.gcstate — State/Province Names and Abbreviation for Geocoding

Sashelp.gcstate --- State/Province Names and Abbreviation for Geocoding




1 MapIDName Char 65 State/province name2 MapIDName2 Char 65 Normalized state/province name3 MapIDNameAbrv Char 5 State/province abbreviation4 ISOname Char 18 ISO country name5 ISOalpha2 Char 2 ISO alpha2 country code6 ISOalpha3 Char 3 ISO alpha3 country code7 ISOname2 Char 18 Normalized ISO country name




MapIDName

MapIDName MapIDName2 Abrv ISOname

Alberta ALBERTA AB CanadaAlaska ALASKA AK United StatesAlabama ALABAMA AL United StatesArkansas ARKANSAS AR United StatesAmerican Samoa AMERICANSAMOA AS United States

ISOalpha2 ISOalpha3 ISOname2

CA CAN CANADAUS USA UNITEDSTATESUS USA UNITEDSTATESUS USA UNITEDSTATESUS USA UNITEDSTATES

Sashelp.gctype — Street Type Abbreviations for US Geocoding F 49

Sashelp.gctype — Street Type Abbreviations for USGeocodingThe Sashelp.gctype data set provides USPS standard street type abbreviations for geocoding. The followingsteps display information about the data set Sashelp.gctype and create Figure 1.31. The data set contains 792observations.

title "Sashelp.gctype --- Street Type Abbreviations for US Geocoding";proc contents data=sashelp.gctype varnum;


title "The First Five Observations Out of 792";proc print data=sashelp.gctype(obs=5) heading=h noobs;run;

Figure 1.31 Sashelp.gctype — Street Type Abbreviations for US Geocoding

Sashelp.gctype --- Street Type Abbreviations for US Geocoding




1 name Char 21 Common prefix/suffix including abbreviations (cleaned)2 type Char 14 USPS standard abbreviation3 GROUP Num 8 Equivalent grouping


name type GROUP

4WDTRL 4WD TRL 2ALLEE ALY 4ALLEY ALY 4ALLY ALY 4ALY ALY 4


Sashelp.geoexm — Primary Street Lookup Data for PROCGEOCODE (Tiger 2012)The Sashelp.geoexm data set provides primary street lookup data for PROC GEOCODE. The followingsteps display information about the data set Sashelp.geoexm and create Figure 1.32. The data set contains37,389 observations.

title "Sashelp.geoexm --- Primary Street Lookup Data for PROC GEOCODE (Tiger 2012)";proc contents data=sashelp.geoexm varnum;


title "The First Five Observations Out of 37389";proc print data=sashelp.geoexm(obs=5) heading=h noobs;run;

Figure 1.32 Sashelp.geoexm — Primary Street Lookup Data for PROC GEOCODE (Tiger 2012)

Sashelp.geoexm --- Primary Street Lookup Data for PROC GEOCODE (Tiger 2012)




1 Name Char 25 Street name2 Name2 Char 22 Street name (normalized)3 City Char 13 $CHAR52. City name4 City2 Char 12 $CHAR52. City name (normalized)5 MapIDNameAbrv Char 2 $CHAR2. State abbreviation6 ZIP Num 8 Z5. ZIP code7 First Num 8 First obs in GEOEXS data set8 Last Num 8 Last obs in GEOEXS data set

Sashelp.geoexm — Primary Street Lookup Data for PROC GEOCODE (Tiger 2012) F 51



Name Name2 City

1 1 Apex1 1 Cary1 1 Holly Springs1 1 Raleigh1 1 Wake Forest

MapIDName

City2 Abrv ZIP First Last

APEX NC . 1 70CARY NC . 71 252HOLLYSPRINGS NC . 253 256RALEIGH NC . 257 266WAKEFOREST NC . 267 278


Sashelp.geoexp — Tertiary Street Lookup Data for PROCGEOCODE (Tiger 2012)The Sashelp.geoexp data set provides tertiary street lookup data for PROC GEOCODE (Tiger 2011). Thefollowing steps display information about the data set Sashelp.geoexp and create Figure 1.33. The data setcontains 827,574 observations.

title "Sashelp.geoexp --- Tertiary Street Lookup Data for PROC GEOCODE (Tiger 2012)";proc contents data=sashelp.geoexp varnum;


title "The First Five Observations Out of 827574";proc print data=sashelp.geoexp(obs=5) heading=h noobs;run;

Figure 1.33 Sashelp.geoexp — Tertiary Street Lookup Data for PROC GEOCODE (Tiger 2012)

Sashelp.geoexp --- Tertiary Street Lookup Data for PROC GEOCODE (Tiger 2012)




1 X Num 8 Longitude (degrees)2 Y Num 8 Latitude (degrees)


X Y

-78.8557 35.7052-78.8557 35.7052-78.8546 35.7057-78.8536 35.7062-78.8511 35.7074

Sashelp.geoexs — Secondary Street Lookup Data for PROC GEOCODE (Tiger 2012) F 53

Sashelp.geoexs — Secondary Street Lookup Data for PROCGEOCODE (Tiger 2012)The Sashelp.geoexs data set provides secondary street lookup data for PROC GEOCODE (Tiger 2011). Thefollowing steps display information about the data set Sashelp.geoexs and create Figure 1.34. The data setcontains 111,927 observations.

title "Sashelp.geoexs --- Secondary Street Lookup Data for PROC GEOCODE (Tiger 2012)";proc contents data=sashelp.geoexs varnum;


title "The First Five Observations Out of 111927";proc print data=sashelp.geoexs(obs=5) heading=h noobs;run;

Figure 1.34 Sashelp.geoexs — Secondary Street Lookup Data for PROC GEOCODE (Tiger 2012)

Sashelp.geoexs --- Secondary Street Lookup Data for PROC GEOCODE (Tiger 2012)




1 PreDirAbrv Char 2 Street name direction prefix2 SufDirAbrv Char 2 Street name direction suffix3 PreTypAbrv Char 9 Street name type prefix4 SufTypAbrv Char 6 Street name type suffix5 TLID Num 8 TIGER/Line ID6 MTFCC Char 5 MAF/TIGER feature class code7 Side Char 1 Side of street8 FromAdd Num 8 Beginning house number9 ToAdd Num 8 Ending house number

10 BlkGrp Num 8 Census 2010 block group11 Block Num 8 Census 2010 tabulation block12 Tract Num 8 Census 2010 tract13 CountyFp Num 8 County FIPS code14 N Num 8 Number of obs in GEOEXP data set15 Start Num 8 First obs in GEOEXP data set




Pre Suf Pre SufDir Dir Typ TypAbrv Abrv Abrv Abrv TLID MTFCC Side

US Hwy 72493942 S1200 LUS Hwy 72493942 S1200 RUS Hwy 72499683 S1200 LUS Hwy 72499683 S1200 RUS Hwy 72499687 S1200 L

From To Blk CountyAdd Add Grp Block Tract Fp N Start

. . 1 1046 53417 183 7 1

. . 1 1045 53417 183 7 8

. . 2 2044 53418 183 5 15

. . 2 2060 53420 183 5 20

. . 1 1045 53417 183 6 25

Sashelp.germ_multi F 55

Sashelp.germ_multiThe following steps display information about the data set Sashelp.germ_multi and create Figure 1.35. Thedata set contains 16 observations.

title "Sashelp.germ_multi";proc contents data=sashelp.germ_multi varnum;


title "The First Five Observations Out of 16";proc print data=sashelp.germ_multi(obs=5) heading=h noobs;run;

Figure 1.35 Sashelp.germ_multi

Sashelp.germ_multi






Term

bergisch gladbachergang und gäbenew yorkerde jurein bezug auf

Role

AdjAdjAdjAdvAdv


Sashelp.gisimp — GISIMP: SAS/GIS Import Data SetThe Sashelp.gisimp data set provides default values used to initialize various fields in the SAS/GIS ImportWindow. See SAS/GIS Spatial Data and Procedure Guide for more information about this data set. Thefollowing steps display information about the data set Sashelp.gisimp and create Figure 1.36. The data setcontains 19 observations.

title "Sashelp.gisimp --- GISIMP: SAS/GIS Import Data Set";proc contents data=sashelp.gisimp varnum;


title "The First Five Observations Out of 19";proc print data=sashelp.gisimp(obs=5) heading=h noobs;run;

title "The TYPE Variable";proc freq data=sashelp.gisimp;

tables TYPE;run;

Figure 1.36 Sashelp.gisimp — GISIMP: SAS/GIS Import Data Set

Sashelp.gisimp --- GISIMP: SAS/GIS Import Data Set



# Variable Type Len

1 TYPE Char 82 FILE_DES Char 503 FILEREF Char 84 REQ Num 85 MAINMETH Char 356 METH Char 87 DTYPE Char 38 SAVE_VAR Num 89 SAVE_LYR Num 8

10 DEP Char 20011 DEFMLIB Char 812 DEFSLIB Char 8

Sashelp.gisimp — GISIMP: SAS/GIS Import Data Set F 57



TYPE FILE_DES FILEREF REQ MAINMETH

Tiger TIGER basic data record TIGER1 1 SASHELP.GISIMP.SUBTIGER.SCLTiger TIGER shape coordinate points TIGER2 1 SASHELP.GISIMP.SUBTIGER.SCLTiger TIGER index to alternate feature names TIGER4 0 SASHELP.GISIMP.SUBTIGER.SCLTiger TIGER feature name list TIGER5 0 SASHELP.GISIMP.SUBTIGER.SCLTiger TIGER additional address and zip code data TIGER6 0 SASHELP.GISIMP.SUBTIGER.SCL

METH DTYPE SAVE_VAR SAVE_LYR DEP DEFMLIB DEFSLIB

TIGER ASC 0 0 Sasuser SasuserTIGER ASC 0 0 Sasuser SasuserTIGER ASC 0 0 Sasuser SasuserTIGER ASC 0 0 Sasuser SasuserTIGER ASC 0 0 Sasuser Sasuser

The TYPE Variable

The FREQ Procedure

Cumulative CumulativeTYPE Frequency Percent Frequency Percent-------------------------------------------------------------ARC 1 5.26 1 5.26DLG 1 5.26 2 10.53DXF 1 5.26 3 15.79Dynamap 5 26.32 8 42.11Genline 1 5.26 9 47.37Genpoint 1 5.26 10 52.63Genpoly 1 5.26 11 57.89Mapinfo 2 10.53 13 68.42SASGRAPH 1 5.26 14 73.68Tiger 5 26.32 19 100.00


Sashelp.gngsmp1 — Sample Chart Data for Graph-N-GoThe Sashelp.gngsmp1 data set provides sample chart data for Graph-N-Go. The following steps displayinformation about the data set Sashelp.gngsmp1 and create Figure 1.37. The data set contains 36 observations.

title "Sashelp.gngsmp1 --- Sample Chart Data for Graph-N-Go";proc contents data=sashelp.gngsmp1 varnum;


title "The First Five Observations Out of 36";proc print data=sashelp.gngsmp1(obs=5) heading=h noobs;run;

title "The group and categoryc Variables";proc freq data=sashelp.gngsmp1;

tables group;tables categoryc;

run;

Figure 1.37 Sashelp.gngsmp1 — Sample Chart Data for Graph-N-Go

Sashelp.gngsmp1 --- Sample Chart Data for Graph-N-Go




1 group Char 7 Group2 categoryc Char 2 Category3 seriesc Char 2 Series4 catc Char 2 Category5 categoryn Num 8 Category6 catn Num 8 Category7 seriesn Num 8 Series8 subgroup Char 10 Subgroup9 response Num 8 Response

10 response1 Num 8 Response 111 response2 Num 8 Response 212 response3 Num 8 Response 313 response4 Num 8 Response 4

Sashelp.gngsmp1 — Sample Chart Data for Graph-N-Go F 59



group categoryc seriesc catc categoryn catn seriesn subgroup

Group 1 B d B 15 15 6 Subgroup 1Group 1 B d B 15 15 6 Subgroup 1Group 1 B d B 15 15 6 Subgroup 2Group 1 A d A 10 10 6 Subgroup 1Group 1 A d A 10 10 6 Subgroup 2

response response1 response2 response3 response4

1 4 3 2 11 4 3 2 13 6 5 4 31 4 3 2 12 5 4 3 2

The group and categoryc Variables

The FREQ Procedure

Group

Cumulative Cumulativegroup Frequency Percent Frequency Percent------------------------------------------------------------Group 1 21 58.33 21 58.33Group 2 15 41.67 36 100.00

Category

Cumulative Cumulativecategoryc Frequency Percent Frequency Percent--------------------------------------------------------------A 4 11.11 4 11.11B 5 13.89 9 25.00C 5 13.89 14 38.89D 4 11.11 18 50.00E 5 13.89 23 63.89F 5 13.89 28 77.78G 2 5.56 30 83.33H 4 11.11 34 94.44I 2 5.56 36 100.00


Sashelp.gngsmp2The Sashelp.gngsmp2 data set provides sample data for Graph-N-Go. The following steps display informa-tion about the data set Sashelp.gngsmp2 and create Figure 1.38. The data set contains 8 observations.

title "Sashelp.gngsmp2";proc contents data=sashelp.gngsmp2 varnum;


title "The First Five Observations Out of 8";proc print data=sashelp.gngsmp2(obs=5) heading=h noobs;run;

Figure 1.38 Sashelp.gngsmp2

Sashelp.gngsmp2




1 Xn Num 8 X-axis2 Xc Char 5 X-axis3 Y Num 8 Y-axis4 ID Char 5 ID5 Y1 Num 86 Y2 Num 87 Y3 Num 88 Y4 Num 89 Date Num 8 DATE.

10 Time Num 8 TIME5.11 DateTime Num 8 DATETIME14.


Xn Xc Y ID Y1 Y2 Y3 Y4 Date Time DateTime

10 b 1 A 10 22 30 38 01JAN00 12:00 01JAN00:12:0020 a 2 A 13 18 30 39 02JAN00 12:01 01JAN00:12:0130 c 5 A 15 16 30 32 03JAN00 12:02 01JAN00:12:0240 g 4 A 18 16 26 36 04JAN00 12:03 01JAN00:12:0350 h 4 B 23 19 24 37 05JAN00 12:04 01JAN00:12:04

Sashelp.gnp — GNP/Macro Data (Quarterly: 1960-1991) F 61

Sashelp.gnp — GNP/Macro Data (Quarterly: 1960-1991)The Sashelp.gnp data set provides GNP/macro data (quarterly: 1960–1991). The following steps displayinformation about the data set Sashelp.gnp and create Figure 1.39. The data set contains 126 observations.

title "Sashelp.gnp --- GNP/Macro Data (Quarterly: 1960-1991)";proc contents data=sashelp.gnp varnum;


title "The First Five Observations Out of 126";proc print data=sashelp.gnp(obs=5) heading=h noobs;run;

Figure 1.39 Sashelp.gnp — GNP/Macro Data (Quarterly: 1960-1991)

Sashelp.gnp --- GNP/Macro Data (Quarterly: 1960-1991)




1 DATE Num 8 YYQ.2 GNP Num 8 gross national product ($billions)3 CONSUMP Num 8 personal consumption expenditures4 INVEST Num 8 gross private domestic investment5 EXPORTS Num 8 net exports of goods and services6 GOVT Num 8 govt purchases of goods and services


DATE GNP CONSUMP INVEST EXPORTS GOVT

1960Q1 516.1 325.5 88.7 4.3 97.61960Q2 514.5 331.6 78.1 5.1 99.61960Q3 517.7 331.7 77.4 6.5 102.11960Q4 513.0 333.8 68.5 7.7 103.01961Q1 517.4 334.4 69.5 8.3 105.3


Sashelp.gridded — Gridded Weight and HeightThe Sashelp.gridded data set provides gridded weight and height. The following steps display informationabout the data set Sashelp.gridded and create Figure 1.40. The data set contains 3,600 observations.

title "Sashelp.gridded --- Gridded Weight and Height";proc contents data=sashelp.gridded varnum;


title "The First Five Observations Out of 3600";proc print data=sashelp.gridded(obs=5) heading=h noobs;run;

Figure 1.40 Sashelp.gridded — Gridded Weight and Height

Sashelp.gridded --- Gridded Weight and Height



# Variable Type Len

1 Height Num 82 Weight Num 83 Count Num 84 Density Num 8


Height Weight Count Density

51.5000 67 0 051.9237 67 0 052.3475 67 0 052.7712 67 0 053.1949 67 0 0

Sashelp.gulfoil — Monthly Oil and Gas Production F 63

Sashelp.gulfoil — Monthly Oil and Gas ProductionThe Sashelp.gulfoil data set provides monthly oil and gas production. The following steps display informationabout the data set Sashelp.gulfoil and create Figure 1.41. The data set contains 3,760 observations.

title "Sashelp.gulfoil --- Monthly Oil and Gas Production";proc contents data=sashelp.gulfoil varnum;


title "The First Five Observations Out of 3760";proc print data=sashelp.gulfoil(obs=5) heading=h noobs;run;

title "The regionname Variable";proc freq data=sashelp.gulfoil;

tables regionname;run;

Figure 1.41 Sashelp.gulfoil — Monthly Oil and Gas Production

Sashelp.gulfoil --- Monthly Oil and Gas Production




1 regionname Char 7 Region within Gulf of Mexico2 protractionname Char 23 Geographic Protraction Area Name3 date Num 8 MONYY7. Time ID (monthly)4 oil Num 8 COMMA12. Oil (Barrels)5 gas Num 8 COMMA12. Gas (Thousand Cubic Feet)


regionname protractionname date oil gas

Central Bay Marchand Area JAN1996 93,830 140,339Central Bay Marchand Area FEB1996 86,595 105,069Central Bay Marchand Area MAR1996 92,827 99,941Central Bay Marchand Area APR1996 88,746 72,398Central Bay Marchand Area MAY1996 67,987 74,693



The regionname Variable

The FREQ Procedure

Region within Gulf of Mexico

Cumulative Cumulativeregionname Frequency Percent Frequency Percent---------------------------------------------------------------Central 2582 68.67 2582 68.67Western 1178 31.33 3760 100.00

Sashelp.heart — Framingham Heart Study F 65

Sashelp.heart — Framingham Heart StudyThe Sashelp.heart data set provides results from the Framingham Heart Study. The following steps displayinformation about the data set Sashelp.heart and create Figure 1.42. The data set contains 5,209 observations.

title "Sashelp.heart --- Framingham Heart Study";proc contents data=sashelp.heart varnum;


title "The First Five Observations Out of 5209";proc print data=sashelp.heart(obs=5) heading=h noobs;run;

title "The Status and DeathCause Variables";proc freq data=sashelp.heart;

tables Status;tables DeathCause;

run;

Figure 1.42 Sashelp.heart — Framingham Heart Study

Sashelp.heart --- Framingham Heart Study




1 Status Char 52 DeathCause Char 26 Cause of Death3 AgeCHDdiag Num 8 Age CHD Diagnosed4 Sex Char 65 AgeAtStart Num 8 Age at Start6 Height Num 87 Weight Num 88 Diastolic Num 89 Systolic Num 8

10 MRW Num 8 Metropolitan Relative Weight11 Smoking Num 812 AgeAtDeath Num 8 Age at Death13 Cholesterol Num 814 Chol_Status Char 10 Cholesterol Status15 BP_Status Char 7 Blood Pressure Status16 Weight_Status Char 11 Weight Status17 Smoking_Status Char 17 Smoking Status




AgeDeath Age At

Status Cause CHDdiag Sex Start Height Weight Diastolic Systolic MRW

Dead Other . Female 29 62.50 140 78 124 121Dead Cancer . Female 41 59.75 194 92 144 183Alive . Female 57 62.25 132 90 170 114Alive . Female 39 65.75 158 80 128 123Alive . Male 42 66.00 156 76 110 116

AgeAt Chol_ Weight_

Smoking Death Cholesterol Status BP_Status Status Smoking_Status

0 55 . Normal Overweight Non-smoker0 57 181 Desirable High Overweight Non-smoker

10 . 250 High High Overweight Moderate (6-15)0 . 242 High Normal Overweight Non-smoker

20 . 281 High Optimal Overweight Heavy (16-25)

The Status and DeathCause Variables

The FREQ Procedure

Cumulative CumulativeStatus Frequency Percent Frequency Percent-----------------------------------------------------------Alive 3218 61.78 3218 61.78Dead 1991 38.22 5209 100.00

Cause of Death

Cumulative CumulativeDeathCause Frequency Percent Frequency Percent------------------------------------------------------------------------------Cancer 539 27.07 539 27.07Cerebral Vascular Disease 378 18.99 917 46.06Coronary Heart Disease 605 30.39 1522 76.44Other 357 17.93 1879 94.37Unknown 112 5.63 1991 100.00

Frequency Missing = 3218

Sashelp.holiday — Holiday Data for En US and En CA Locale F 67

Sashelp.holiday — Holiday Data for En US and En CA LocaleThe Sashelp.holiday data set provides holiday data for EN_US and EN_CA locale. The following stepsdisplay information about the data set Sashelp.holiday and create Figure 1.43. The data set contains 27observations.

title "Sashelp.holiday --- Holiday Data for En US and En CA Locale";proc contents data=sashelp.holiday varnum;


title "The First Five Observations Out of 27";proc print data=sashelp.holiday(obs=5) heading=h noobs;run;

Figure 1.43 Sashelp.holiday — Holiday Data for En US and En CA Locale

Sashelp.holiday --- Holiday Data for En US and En CA Locale



# Variable Type Len

1 name Char 322 desc Char 643 category Char 324 begin Num 85 end Num 86 month Num 87 day Num 88 rule Num 8


name desc

BOXING Boxing DayCANADA Canadian Independence DayCANADAOBSERVED Canadian Independence Day observedCHRISTMAS ChristmasCHRISTMAS Christmas

category begin end month day rule

en_CA . . 12 26 0en_CA . . 7 1 0en_CA . . 7 1 8en_US . . 12 25 0en_CA . . 12 25 0


Sashelp.humid — Source: W. L. Donn, Meteorology, 4thEditionThe following steps display information about the data set Sashelp.humid and create Figure 1.44. The dataset contains 584 observations.

title "Sashelp.humid --- Source: W. L. Donn, Meteorology, 4th Edition";proc contents data=sashelp.humid varnum;


title "The First Five Observations Out of 584";proc print data=sashelp.humid(obs=5) heading=h noobs;run;

Figure 1.44 Sashelp.humid — Source: W. L. Donn, Meteorology, 4th Edition

Sashelp.humid --- Source: W. L. Donn, Meteorology, 4th Edition




1 BulbTemp Num 8 Wet-Bulb Temperature (F)2 Humidity Num 8 Relative Humidity (%)3 AirTemp Num 8 Air Temperature (F)4 ColorVar Char 8


Bulb AirTemp Humidity Temp ColorVar

1 67 0 CX0000FF2 33 0 CX0000FF3 1 0 CX0000FF1 73 5 CX0900F62 46 5 CX0900F6

Sashelp.iris — Fisher’s Iris Data (1936) F 69

Sashelp.iris — Fisher’s Iris Data (1936)The Sashelp.Iris data set (Fisher 1936) is widely used for examples of discriminant analysis and clusteranalysis. The data are measurements in millimeters of the sepal length, sepal width, petal length, and petalwidth of 50 iris specimens from each of three species: Iris setosa, I. versicolor, and I. virginica. The followingsteps display information about the data set Sashelp.iris and create Figure 1.45. The data set contains 150observations.

title "Sashelp.iris --- Fisher's Iris Data (1936)";proc contents data=sashelp.iris varnum;


title "The First Five Observations Out of 150";proc print data=sashelp.iris(obs=5) heading=h noobs;run;

title "The Species Variable";proc freq data=sashelp.iris;

tables Species;run;

Figure 1.45 Sashelp.iris — Fisher’s Iris Data (1936)

Sashelp.iris --- Fisher's Iris Data (1936)




1 Species Char 10 Iris Species2 SepalLength Num 8 Sepal Length (mm)3 SepalWidth Num 8 Sepal Width (mm)4 PetalLength Num 8 Petal Length (mm)5 PetalWidth Num 8 Petal Width (mm)


Sepal Sepal Petal PetalSpecies Length Width Length Width

Setosa 50 33 14 2Setosa 46 34 14 3Setosa 46 36 10 2Setosa 51 33 17 5Setosa 55 35 13 2



The Species Variable

The FREQ Procedure

Iris Species

Cumulative CumulativeSpecies Frequency Percent Frequency Percent---------------------------------------------------------------Setosa 50 33.33 50 33.33Versicolor 50 33.33 100 66.67Virginica 50 33.33 150 100.00

Sashelp.ital_multi F 71

Sashelp.ital_multiThe following steps display information about the data set Sashelp.ital_multi and create Figure 1.46. Thedata set contains 520 observations.

title "Sashelp.ital_multi";proc contents data=sashelp.ital_multi varnum;


title "The First Five Observations Out of 520";proc print data=sashelp.ital_multi(obs=5) heading=h noobs;run;

Figure 1.46 Sashelp.ital_multi

Sashelp.ital_multi






Term

a causa deglia causa deia causa dela causa dell'a causa della

Role

AdjAdjAdjAdjAdj


Sashelp.jaThe Sashelp.ja data set provides a DBCS translation tables. JA is for Japanese. The following steps displayinformation about the data set Sashelp.ja and create Figure 1.47. The data set contains 7,923 observations.

title "Sashelp.ja";proc contents data=sashelp.ja varnum;


title "The First Five Observations Out of 7923";proc print data=sashelp.ja(obs=5) heading=h noobs;run;

Figure 1.47 Sashelp.ja

Sashelp.ja



# Variable Type Len

1 unicode Char 62 euc Char 43 ibm Char 44 jis Char 45 pcibm Char 4


unicode euc ibm jis pcibm

U+3000 A1A1 4040 2121 8140U+3001 A1A2 4344 2122 8141U+3002 A1A3 4341 2123 8142U+FF0C A1A4 426B 2124 8143U+FF0E A1A5 424B 2125 8144

Sashelp.junkmail — Classifying Email As Junk Or Not F 73

Sashelp.junkmail — Classifying Email As Junk Or NotThe Sashelp.JunkMail data set comes from a study that classifies whether an e-mail is junk e-mail (coded as1) or not (coded as 0). The data were collected in Hewlett-Packard labs and donated by George Forman. Thedata set contains 4,601 observations with 58 variables. The response variable is a binary indicator of whetheran e-mail is considered spam or not. The 57 variables are continuous variables that record frequencies ofsome common words and characters and lengths of uninterrupted sequences of capital letters in e-mails. Thefollowing steps display information about the data set Sashelp.junkmail and create Figure 1.48. The data setcontains 4,601 observations.

title "Sashelp.junkmail --- Classifying Email As Junk Or Not";proc contents data=sashelp.junkmail varnum;


title "The First Five Observations Out of 4601";proc print data=sashelp.junkmail(obs=5) heading=h noobs;run;


Figure 1.48 Sashelp.junkmail — Classifying Email As Junk Or Not

Sashelp.junkmail --- Classifying Email As Junk Or Not




1 Test Num 8 0 - Training, 1 - Test2 Make Num 83 Address Num 84 All Num 85 _3D Num 8 3D6 Our Num 87 Over Num 88 Remove Num 89 Internet Num 8

10 Order Num 811 Mail Num 812 Receive Num 813 Will Num 814 People Num 815 Report Num 816 Addresses Num 817 Free Num 818 Business Num 819 Email Num 820 You Num 821 Credit Num 822 Your Num 823 Font Num 824 _000 Num 8 00025 Money Num 826 HP Num 827 HPL Num 828 George Num 829 _650 Num 8 65030 Lab Num 831 Labs Num 832 Telnet Num 833 _857 Num 8 85734 Data Num 835 _415 Num 8 41536 _85 Num 8 8537 Technology Num 838 _1999 Num 8 199939 Parts Num 840 PM Num 841 Direct Num 842 CS Num 843 Meeting Num 844 Original Num 845 Project Num 846 RE Num 847 Edu Num 8

Sashelp.junkmail — Classifying Email As Junk Or Not F 75


Sashelp.junkmail --- Classifying Email As Junk Or Not




48 Table Num 849 Conference Num 850 Semicolon Num 851 Paren Num 852 Bracket Num 853 Exclamation Num 854 Dollar Num 855 Pound Num 856 CapAvg Num 8 Capital Run Length Average57 CapLong Num 8 Capital Run Length Longest58 CapTotal Num 8 Capital Run Length Total59 Class Num 8 0 - Not Junk, 1 - Junk




Test Make Address All _3D Our Over Remove Internet Order Mail Receive Will

1 0.00 0.64 0.64 0 0.32 0.00 0.00 0.00 0.00 0.00 0.00 0.640 0.21 0.28 0.50 0 0.14 0.28 0.21 0.07 0.00 0.94 0.21 0.791 0.06 0.00 0.71 0 1.23 0.19 0.19 0.12 0.64 0.25 0.38 0.450 0.00 0.00 0.00 0 0.63 0.00 0.31 0.63 0.31 0.63 0.31 0.310 0.00 0.00 0.00 0 0.63 0.00 0.31 0.63 0.31 0.63 0.31 0.31

People Report Addresses Free Business Email You Credit Your Font _000 Money

0.00 0.00 0.00 0.32 0.00 1.29 1.93 0.00 0.96 0 0.00 0.000.65 0.21 0.14 0.14 0.07 0.28 3.47 0.00 1.59 0 0.43 0.430.12 0.00 1.75 0.06 0.06 1.03 1.36 0.32 0.51 0 1.16 0.060.31 0.00 0.00 0.31 0.00 0.00 3.18 0.00 0.31 0 0.00 0.000.31 0.00 0.00 0.31 0.00 0.00 3.18 0.00 0.31 0 0.00 0.00

HP HPL George _650 Lab Labs Telnet _857 Data _415 _85 Technology _1999 Parts

0 0 0 0 0 0 0 0 0 0 0 0 0.00 00 0 0 0 0 0 0 0 0 0 0 0 0.07 00 0 0 0 0 0 0 0 0 0 0 0 0.00 00 0 0 0 0 0 0 0 0 0 0 0 0.00 00 0 0 0 0 0 0 0 0 0 0 0 0.00 0

PM Direct CS Meeting Original Project RE Edu Table Conference Semicolon

0 0.00 0 0 0.00 0 0.00 0.00 0 0 0.000 0.00 0 0 0.00 0 0.00 0.00 0 0 0.000 0.06 0 0 0.12 0 0.06 0.06 0 0 0.010 0.00 0 0 0.00 0 0.00 0.00 0 0 0.000 0.00 0 0 0.00 0 0.00 0.00 0 0 0.00

Cap Cap CapParen Bracket Exclamation Dollar Pound Avg Long Total Class

0.000 0 0.778 0.000 0.000 3.756 61 278 10.132 0 0.372 0.180 0.048 5.114 101 1028 10.143 0 0.276 0.184 0.010 9.821 485 2259 10.137 0 0.137 0.000 0.000 3.537 40 191 10.135 0 0.135 0.000 0.000 3.537 40 191 1

Sashelp.ko F 77

Sashelp.koThe Sashelp.ko data set provides a DBCS translation tables. KO is for Korean. The following steps displayinformation about the data set Sashelp.ko and create Figure 1.49. The data set contains 2,561 observations.

title "Sashelp.ko";proc contents data=sashelp.ko varnum;


title "The First Five Observations Out of 2561";proc print data=sashelp.ko(obs=5) heading=h noobs;run;

Figure 1.49 Sashelp.ko

Sashelp.ko



# Variable Type Len

1 unicode Char 62 euc Char 43 ibm Char 44 pcibm Char 4


unicode euc ibm pcibm

U+C90E A1A0 4140 8140U+3002 A1A3 4142 8142U+00B7 A1A4 4143 8143U+2025 A1A5 4144 8144U+2026 A1A6 4145 8145


Sashelp.lake — Sample 3D Data of Lake DepthThe Sashelp.lake data set provides a sample 3D data of lake depth. The following steps display informationabout the data set Sashelp.lake and create Figure 1.50. The data set contains 315 observations.

title "Sashelp.lake --- Sample 3D Data of Lake Depth";proc contents data=sashelp.lake varnum;


title "The First Five Observations Out of 315";proc print data=sashelp.lake(obs=5) heading=h noobs;run;

Figure 1.50 Sashelp.lake — Sample 3D Data of Lake Depth

Sashelp.lake --- Sample 3D Data of Lake Depth



# Variable Type Len

1 Width Num 82 Length Num 83 Depth Num 8


Width Length Depth

0 0.0 0.00000000 0.5 0.00000000 1.0 0.00000000 1.5 -.00597730 2.0 0.0000000

Sashelp.leutest — Leukemia Data Set - Validation Data F 79

Sashelp.leutest — Leukemia Data Set - Validation DataThe Sashelp.LeuTrain and Sashelp.LeuTest data sets provide microarray data from Zou and Hastie (2005).The Sashelp.LeuTrain data set consists of 7129 genes and 38 training samples, and the Sashelp.LeuTest dataset consists of the same 7129 genes and 34 testing samples. Among the 38 training samples, 27 are type 1leukemia (acute lymphoblastic leukemia, coded in the data as 1) and 11 are type 2 leukemia (acute myeloidleukemia, coded in the data as –1). These data are not displayed because of the number of variables.

Sashelp.leutrain — Leukemia Data Set - Training DataThe Sashelp.LeuTrain and Sashelp.LeuTest data sets provide microarray data from Zou and Hastie (2005).The Sashelp.LeuTrain data set consists of 7129 genes and 38 training samples, and the Sashelp.LeuTest dataset consists of the same 7129 genes and 34 testing samples. Among the 38 training samples, 27 are type 1leukemia (acute lymphoblastic leukemia, coded in the data as 1) and 11 are type 2 leukemia (acute myeloidleukemia, coded in the data as –1). These data are not displayed because of the number of variables.


Sashelp.lthemeThe following steps display information about the data set Sashelp.ltheme and create Figure 1.51. The dataset contains 13 observations.

title "Sashelp.ltheme";proc contents data=sashelp.ltheme varnum;


title "The First Five Observations Out of 13";proc print data=sashelp.ltheme(obs=5) heading=h noobs;run;

Figure 1.51 Sashelp.ltheme

Sashelp.ltheme




1 CFCC Char 12 NAME Char 21 LANDMARKS


CFCC NAME

V Housing UnitZ PrisonY Hospitala Churchb Educational

Sashelp.macrs10 F 81

Sashelp.macrs10The Sashelp.macrs10 data set provides a year and rate variable. The following steps display informationabout the data set Sashelp.macrs10 and create Figure 1.52. The data set contains 11 observations.

title "Sashelp.macrs10";proc contents data=sashelp.macrs10 varnum;


title "The First Five Observations Out of 11";proc print data=sashelp.macrs10(obs=5) heading=h noobs;run;

Figure 1.52 Sashelp.macrs10

Sashelp.macrs10



# Variable Type Len

1 YEAR Num 82 RATE Num 8


YEAR RATE

1 10.002 18.003 14.404 11.525 9.22







Sashelp.macrs15



# Variable Type Len



YEAR RATE

1 5.002 9.503 8.554 7.705 6.93

Sashelp.macrs20 F 83






Sashelp.macrs20



# Variable Type Len



YEAR RATE

1 3.7502 7.2193 6.6774 6.1775 5.713





title "The Full Data Set";proc print data=sashelp.macrs3 heading=h noobs;run;


Sashelp.macrs3



# Variable Type Len


The Full Data Set

YEAR RATE

1 33.332 44.453 14.814 7.41

Sashelp.macrs5 F 85






Sashelp.macrs5



# Variable Type Len



YEAR RATE

1 20.002 32.003 19.204 11.525 11.52







Sashelp.macrs7



# Variable Type Len



YEAR RATE

1 14.292 24.493 17.494 12.495 8.93

Sashelp.margarin — Margarine Purchase Data F 87

Sashelp.margarin — Margarine Purchase DataThe Sashelp.Margarin data set is a scanner panel data set that lists purchases of margarine. There are 313households and a total of 3,405 purchases. The variable HouseID represents the household ID; each householdmade at least five purchases, which are defined by the choice set variable Set. The variable Choice representsthe choice that households made among the six margarine brands for each purchase or choice set. Thevariable Brand has the value ‘PPK’ for Parkay stick, ‘PBB’ for Blue Bonnet stick, ‘PFL’ for Fleischmann’sstick, ‘PHse’ for the house brand stick, ‘PGen’ for the generic stick, and ‘PSS’ for Shedd’s Spread tub.The variable LogPrice is the logarithm of the product price. The variables LogInc and FamSize provideinformation about household income and family size, respectively. The following steps display informationabout the data set Sashelp.margarin and create Figure 1.58. The data set contains 20,430 observations.

title "Sashelp.margarin --- Margarine Purchase Data";proc contents data=sashelp.margarin varnum;


title "The First Five Observations Out of 20430";proc print data=sashelp.margarin(obs=5) heading=h noobs;run;

Figure 1.58 Sashelp.margarin — Margarine Purchase Data

Sashelp.margarin --- Margarine Purchase Data



# Variable Type Len

1 HouseID Num 82 Set Num 83 Choice Num 84 Brand Char 85 LogPrice Num 86 LogInc Num 87 FamSize Num 8


FamHouseID Set Choice Brand LogPrice LogInc Size

2100016 1 1 PPk -0.41552 3.48124 22100016 1 0 PBB -0.40048 3.48124 22100016 1 0 PFl 0.08618 3.48124 22100016 1 0 PHse -0.56212 3.48124 22100016 1 0 PGen -1.02165 3.48124 2


Sashelp.mdv — Sales Data and ForecastThe Sashelp.mdv data set provides sales data and forecast. The following steps display information about thedata set Sashelp.mdv and create Figure 1.59. The data set contains 128 observations.

title "Sashelp.mdv --- Sales Data and Forecast";proc contents data=sashelp.mdv varnum;


title "The First Five Observations Out of 128";proc print data=sashelp.mdv(obs=5) heading=h noobs;run;

Figure 1.59 Sashelp.mdv — Sales Data and Forecast

Sashelp.mdv --- Sales Data and Forecast



# Variable Type Len Format Informat

1 CODE Char 102 ORIGCITY Char 83 COUNTRY Char 144 TYPE Char 145 CITY Char 146 COMPANY Char 50 $30.7 SHIPDATE Num 8 DATE7. DATE.8 SALES94 Num 8 COMMA10.29 MONTH Num 8

10 YEAR Num 811 SALES93 Num 8 COMMA10.212 SALES95 Num 8 COMMA10.213 _4CAST96 Num 8 COMMA10.214 DAY Num 8

Sashelp.mdv — Sales Data and Forecast F 89



CODE ORIGCITY COUNTRY TYPE CITY COMPANY

NEXT DAY LONDON AUSTRALIA MD11 ACTON OUTWAY COMPANYNEXT DAY LONDON AUSTRALIA DC10 ACTON AUSTRALIAN WAY HOSPITALSECOND DAY SAN FRAN AUSTRALIA A300 ACTON NATIONAL GROVE UNIVERSITYTHIRD DAY NEW YORK AUSTRALIA MD11 MELBOURNE STATE INCOME OFFICESECOND DAY SYDNEY AUSTRALIA DC10 MELBOURNE MELROSE TECH

SHIPDATE SALES94 MONTH YEAR SALES93 SALES95 _4CAST96 DAY

20APR95 523.24 4 1995 288.24 1,500.24 1,660.57 2022APR95 523.24 4 1995 499.24 804.24 874.62 2203DEC95 523.24 12 1995 198.24 1,308.24 1,340.82 309SEP95 1,170.00 9 1995 529.00 1,596.00 1,876.61 922NOV95 251.29 11 1995 628.71 1,178.29 1,320.58 22


Sashelp.mileages — Flying Mileages Between Ten US CitiesThe Sashelp.Mileages data set contains a table of flying mileages between 10 US cities. This data setis frequently used to illustrate cluster analysis and multidimensional scaling. The following steps displayinformation about the data set Sashelp.mileages and create Figure 1.60. The data set contains 10 observations.

title "Sashelp.mileages --- Flying Mileages Between Ten US Cities";proc contents data=sashelp.mileages varnum;


title "The First Five Observations Out of 10";proc print data=sashelp.mileages(obs=5) heading=h noobs;run;

Figure 1.60 Sashelp.mileages — Flying Mileages Between Ten US Cities

Sashelp.mileages --- Flying Mileages Between Ten US Cities



# Variable Type Len

1 Atlanta Num 82 Chicago Num 83 Denver Num 84 Houston Num 85 LosAngeles Num 86 Miami Num 87 NewYork Num 88 SanFrancisco Num 89 Seattle Num 8

10 WashingtonDC Num 811 City Char 15


Los New San WashingtonAtlanta Chicago Denver Houston Angeles Miami York Francisco Seattle DC City

0 . . . . . . . . . Atlanta587 0 . . . . . . . . Chicago

1212 920 0 . . . . . . . Denver701 940 879 0 . . . . . . Houston

1936 1745 831 1374 0 . . . . . Los Angeles

Sashelp.mon1001 — M-Competition 1001 Series, Monthly F 91

Sashelp.mon1001 — M-Competition 1001 Series, MonthlyThe Sashelp.mon1001 data set provides m-competition 1001 series, monthly. The following steps dis-play information about the data set Sashelp.mon1001 and create Figure 1.61. The data set contains 132observations.

title "Sashelp.mon1001 --- M-Competition 1001 Series, Monthly";proc contents data=sashelp.mon1001 varnum;


title "The First Five Observations Out of 132";proc print data=sashelp.mon1001(obs=5) heading=h noobs;run;


Figure 1.61 Sashelp.mon1001 — M-Competition 1001 Series, Monthly

Sashelp.mon1001 --- M-Competition 1001 Series, Monthly



# Variable Type Len

1 T Num 42 S0387 Num 53 S0388 Num 54 S0389 Num 55 S0390 Num 56 S0391 Num 57 S0393 Num 58 S0394 Num 59 S0395 Num 510 S0396 Num 511 S0397 Num 512 S0398 Num 513 S0401 Num 514 S0403 Num 515 S0404 Num 516 S0405 Num 517 S0406 Num 518 S0407 Num 519 S0409 Num 520 S0410 Num 521 S0412 Num 522 S0413 Num 523 S0414 Num 524 S0415 Num 525 S0416 Num 526 S0417 Num 527 S0418 Num 528 S0419 Num 529 S0420 Num 530 S0421 Num 531 S0422 Num 532 S0423 Num 533 S0426 Num 534 S0427 Num 535 S0428 Num 536 S0429 Num 537 S0434 Num 538 S0435 Num 539 S0436 Num 540 S0437 Num 541 S0438 Num 542 S0439 Num 543 S0440 Num 544 S0441 Num 545 S0442 Num 546 S0444 Num 547 S0445 Num 548 S0446 Num 549 S0448 Num 550 S0449 Num 551 S0450 Num 552 S0451 Num 5






# Variable Type Len

53 S0452 Num 554 S0453 Num 555 S0454 Num 556 S0455 Num 557 S0456 Num 558 S0457 Num 559 S0458 Num 560 S0459 Num 561 S0460 Num 562 S0461 Num 563 S0462 Num 564 S0463 Num 565 S0464 Num 566 S0465 Num 567 S0466 Num 568 S0467 Num 569 S0468 Num 570 S0469 Num 571 S0470 Num 572 S0471 Num 573 S0472 Num 574 S0473 Num 575 S0474 Num 576 S0475 Num 577 S0476 Num 578 S0477 Num 579 S0479 Num 580 S0480 Num 581 S0481 Num 582 S0486 Num 583 S0487 Num 584 S0488 Num 585 S0489 Num 586 S0490 Num 587 S0491 Num 588 S0492 Num 589 S0493 Num 590 S0495 Num 591 S0496 Num 592 S0497 Num 593 S0498 Num 594 S0499 Num 595 S0501 Num 596 S0503 Num 597 S0504 Num 598 S0505 Num 599 S0506 Num 5

100 S0507 Num 5101 S0508 Num 5102 S0509 Num 5103 S0510 Num 5104 S0511 Num 5






# Variable Type Len

105 S0512 Num 5106 S0513 Num 5107 S0514 Num 5108 S0515 Num 5109 S0516 Num 5110 S0520 Num 5111 S0521 Num 5112 S0522 Num 5113 S0523 Num 5114 S0524 Num 5115 S0525 Num 5116 S0526 Num 5117 S0527 Num 5118 S0528 Num 5119 S0529 Num 5120 S0533 Num 5121 S0534 Num 5122 S0536 Num 5123 S0537 Num 5124 S0538 Num 5125 S0539 Num 5126 S0540 Num 5127 S0541 Num 5128 S0542 Num 5129 S0543 Num 5130 S0544 Num 5131 S0545 Num 5132 S0546 Num 5133 S0547 Num 5134 S0548 Num 5135 S0552 Num 5136 S0554 Num 5137 S0557 Num 5138 S0559 Num 5139 S0560 Num 5140 S0562 Num 5141 S0563 Num 5142 S0565 Num 5143 S0566 Num 5144 S0567 Num 5145 S0569 Num 5146 S0570 Num 5147 S0571 Num 5148 S0572 Num 5149 S0573 Num 5150 S0574 Num 5151 S0575 Num 5152 S0576 Num 5153 S0577 Num 5154 S0578 Num 5155 S0579 Num 5156 S0580 Num 5






# Variable Type Len







# Variable Type Len

365 S0907 Num 5366 S0908 Num 5367 S0909 Num 5368 S0910 Num 5369 S0911 Num 5370 S0912 Num 5371 S0913 Num 5372 S0914 Num 5373 S0927 Num 5374 S0931 Num 5375 S0932 Num 5376 S0933 Num 5377 S0934 Num 5378 S0936 Num 5379 S0937 Num 5380 S0938 Num 5381 S0939 Num 5382 S0940 Num 5383 S0952 Num 5384 S0953 Num 5385 S0954 Num 5386 S0955 Num 5387 S0956 Num 5388 S0957 Num 5389 S0958 Num 5390 S0978 Num 5391 S0980 Num 5392 S0981 Num 5393 S0982 Num 5394 S0983 Num 5395 S0984 Num 5396 S0985 Num 5397 S0986 Num 5398 S0990 Num 5399 S0991 Num 5400 S0992 Num 5401 S0993 Num 5402 S0994 Num 5403 S0995 Num 5404 S0997 Num 5405 S0998 Num 5406 S0999 Num 5407 S1001 Num 5




T S0387 S0388 S0389 S0390 S0391 S0393 S0394 S0395 S0396 S0397 S0398 S0401 S0403

1 409 3334 33.6576 341 117497 3341 5237 1807 23.9000 13.9000 198 43.2000 12908.402 419 3363 30.9665 793 153276 4352 3612 1702 23.3000 10.0000 212 44.8000 11486.203 339 3395 38.0410 1687 241443 9148 4385 1760 18.1000 6.6000 154 43.1000 10930.804 421 3831 37.3077 2477 218709 11714 6479 1675 21.6000 10.4000 131 41.9000 9801.455 474 3292 33.8624 2007 202896 10878 13286 1701 32.4000 11.4000 181 44.4000 6128.94

S0404 S0405 S0406 S0407 S0409 S0410 S0412 S0413 S0414 S0415 S0416 S0417 S0418 S0419

9223.61 10368.20 2356.89 34.5600 443 890 867 264 32 670 796 2276 403 6778621.12 9479.09 2042.34 31.8699 469 984 638 234 28 634 881 2106 372 667

10519.20 11166.30 1065.32 39.3560 251 2440 926 234 30 655 891 2190 381 7119580.23 10103.30 2321.67 36.2121 286 4124 964 248 25 658 977 2017 465 7756334.12 6652.24 539.57 33.0508 342 3197 709 271 32 729 894 2244 402 706

S0420 S0421 S0422 S0423 S0426 S0427 S0428 S0429 S0434 S0435 S0436 S0437 S0438

105 692 676 587 13224 13224.00 7546 7546.00 25857 1264 2107 37.4000 31120.59117 696 667 634 14302 14404.20 8105 8162.89 27845 1150 2097 33.6000 31120.59128 809 711 600 17824 17824.00 9750 9750.00 27205 1384 2135 46.0000 31794.69149 886 775 664 20295 20295.00 10610 10610.00 27612 1651 2165 57.6000 28286.09179 835 706 633 19384 19384.00 10484 10484.00 14103 1374 2205 46.2000 29434.19

S0439 S0440 S0441 S0442 S0444 S0445 S0446 S0448 S0449 S0450 S0451 S0452

8892.00 244.893 573.768 11184 29.8000 90000 3672.76 2064.54 4618.28 6419.94 13936.09 695.0418821.00 244.893 638.439 12094 27.3000 87000 3464.47 1996.29 4759.95 5692.17 14330.50 686.3929799.65 255.901 679.047 11542 37.1000 111000 3239.43 2252.22 6029.44 6224.04 19923.00 731.0849846.77 230.992 648.215 10245 41.3000 108000 3353.19 2195.35 4597.24 5944.83 19040.89 797.208

10890.20 238.291 534.665 9031 38.3000 77000 3381.60 2178.29 3919.20 5887.16 16874.00 726.125

S0453 S0454 S0455 S0456 S0457 S0458 S0459 S0460 S0461 S0462 S0463

4742.66 2783.70 52075.59 288.734 27366.59 18964.59 144.634 1287.41 5800.54 395.884 12344.005061.20 3746.94 53003.88 262.668 27311.59 19730.69 134.772 1339.45 5900.04 509.216 13970.705627.49 6404.38 60964.88 296.755 29605.19 22483.19 149.564 1428.24 6295.79 844.553 13587.905388.58 5074.96 57644.28 262.668 26610.19 18554.59 139.703 1422.12 6709.63 639.625 13109.505468.22 4526.14 57789.59 326.831 26792.00 14497.50 161.069 1220.05 6397.55 878.708 12152.59

S0464 S0465 S0466 S0467 S0468 S0469 S0470 S0471 S0472 S0473 S0474 S0475

10076.20 4514.25 126754 2027.43 16180.40 5732.77 1081.98 14645.20 2496.26 6484 1871 515609639.31 5481.76 131824 1892.26 14346.20 5658.64 1239.20 23412.30 2485.05 7083 2272 44555

10330.09 4837.56 133024 2041.41 15686.70 6029.29 1505.19 41736.59 2639.59 7539 2005 5218510934.90 4656.60 139420 1966.84 14983.00 6572.92 1538.85 45624.19 2649.48 5078 1930 4991311559.90 4311.58 141575 1938.87 14837.59 5988.11 1215.61 42416.09 2313.73 4043 1787 42271




S0476 S0477 S0479 S0480 S0481 S0486 S0487 S0488 S0489 S0490 S0491 S0492 S0493 S0495

8458 490 184 876 24 10713 464 9904 387.000 292.900 60.4000 85.600 3738.30 3291317323 1030 370 1653 77 12843 347 10084 403.000 297.900 60.8000 86.500 3162.30 3344657796 930 379 1280 54 20673 581 14000 471.100 351.400 77.8000 101.800 2522.70 3779287339 754 268 1226 43 18157 624 12296 389.100 286.500 60.9000 81.200 2065.30 3454696650 690 245 1133 14 17551 590 11439 396.300 293.500 62.1000 85.800 1954.80 362411

S0496 S0497 S0498 S0499 S0501 S0503 S0504 S0505 S0506 S0507 S0508 S0509 S0510 S0511 S0512

834.055 6910 5231 780 1348 24 7184 420 13858 2876 13858 69 2613 950 1193809.374 7035 5349 815 1266 83 7201 840 14919 3175 14919 239 2629 1205 1202840.438 7717 5903 929 1345 140 7395 986 15451 3295 15451 341 2606 1226 1305820.438 6651 5108 851 1254 166 7457 1327 14760 3085 14760 350 2774 1083 1223809.374 7304 5634 921 1370 163 7784 1093 16263 3793 16263 432 2606 1212 1321

S0513 S0514 S0515 S0516 S0520 S0521 S0522 S0523 S0524 S0525 S0526 S0527

2219 461 420 68 11072.90 48238.19 1656.32 5122.23 17104.19 1210.38 1759.81 17686.091964 959 840 63 10903.30 52827.09 1593.20 5656.23 14984.00 1350.64 1912.56 21199.692164 1113 986 74 11927.30 61600.19 1572.03 6885.36 21199.89 1470.77 2171.54 24896.502282 1465 1327 71 11097.70 42265.69 1347.01 6103.71 21409.50 1719.11 2191.08 16016.302289 1130 1093 74 11011.30 41705.59 1535.81 5939.05 17012.80 2065.77 1804.54 15269.30

S0528 S0529 S0533 S0534 S0536 S0537 S0538 S0539 S0540 S0541 S0542 S0543 S0544

1702.22 9584.80 33 60 254 189 77.0173 103.427 66.4395 145.047 90.6368 102.701 151.5111590.88 10440.09 38 147 237 163 79.8517 103.151 63.8469 140.035 91.1207 113.175 148.8841717.20 11640.59 40 147 283 224 72.7308 99.867 65.7134 138.479 89.4960 110.306 148.1921640.12 10627.90 37 106 300 281 69.8962 103.738 68.9975 139.758 87.5602 110.479 144.4591657.25 10213.30 37 113 273 221 73.1457 104.741 74.6666 144.528 90.6713 109.234 146.740

S0545 S0546 S0547 S0548 S0552 S0554 S0557 S0559 S0560 S0562 S0563 S0565 S0566

260.641 123.753 205.471 2561 4472 467214 8263 1424 107.600 7.50000 17.8000 22910.89 543.752265.792 124.859 215.323 2974 5351 197073 7803 1666 105.500 6.40000 18.5000 22911.59 456.279250.651 127.210 204.572 2724 6366 261792 10150 2175 102.500 7.00000 18.0000 26539.39 527.203264.962 133.121 212.627 2983 4927 132044 11288 1690 99.200 6.70000 17.4000 25068.09 583.942267.002 137.856 208.271 3426 4940 161976 10933 1106 99.400 5.50000 16.2000 27748.00 581.578

S0567 S0569 S0570 S0571 S0572 S0573 S0574 S0575 S0576 S0577 S0578

5096.30 921.19 57.4853 23746.89 4961.82 13251.40 16485.09 13805.20 60190.69 3009.00 1129.075378.42 850.35 53.7925 23746.89 5856.50 11529.59 14040.80 14915.59 60238.38 2995.48 4395.966109.78 870.03 53.2551 27454.09 6205.32 12298.20 14487.59 16819.00 69407.25 3181.77 8356.955924.60 1063.10 51.6254 23829.30 4853.29 11006.90 13268.50 14124.30 56226.09 3193.69 6154.355130.37 919.45 50.8389 24712.59 4476.57 10099.90 13578.09 9562.55 50286.00 2788.98 9032.55




S0579 S0580 S0581 S0584 S0585 S0587 S0588 S0589 S0590 S0591 S0592 S0593

1259.13 12480.70 7850.63 2916.44 654.054 45.8140 139.000 3986 987.63 2.81500 10.2400 57.70001337.83 12285.00 7273.40 2901.56 778.986 50.8030 125.500 4175 941.24 2.67200 10.7500 55.70001416.52 12325.00 8055.33 3299.59 683.450 57.8190 128.700 4175 1089.15 2.75500 11.6200 66.10001259.13 12269.40 8092.68 2927.60 734.893 79.1130 137.100 4500 940.57 2.72100 12.2200 61.30001101.74 12327.20 6288.62 2830.88 712.846 88.3730 111.000 4625 962.08 2.94600 10.4800 61.6000

S0594 S0595 S0596 S0597 S0598 S0599 S0600 S0601 S0605 S0606 S0607 S0608 S0609

79.9000 246.900 61.0000 63.1000 140 0.94000 39.9000 5.1400 84 109 90 101 111379.5000 240.300 61.1000 64.9000 134 1.12000 39.2000 10.5000 87 110 102 107 102396.1000 284.800 59.1000 67.3000 144 1.02000 39.5000 9.9400 88 112 99 102 111484.6000 255.200 62.1000 72.8000 140 1.20000 41.8000 10.7300 98 113 108 108 105976.3000 244.600 54.8000 65.4000 139 1.17000 42.5000 11.7100 104 120 109 116 1083

S0610 S0611 S0613 S0614 S0615 S0616 S0617 S0618 S0619 S0620 S0621 S0622 S0623

5520 2597 112 103 67227 1081.76 1893.49 1646.67 3507.98 8522.86 20265.59 9127.33 148.8944855 2377 115 110 60968 1080.11 1650.29 1512.32 3438.24 8007.64 20299.09 14356.00 242.8115123 2513 115 109 65889 1353.93 1785.79 1681.36 4446.06 8687.70 21739.39 16131.90 371.0885160 2364 122 115 63394 1326.56 1666.75 1400.34 4340.20 8931.27 19365.89 15291.09 475.3144933 2454 119 111 65381 1406.02 1622.50 1607.49 4535.20 7479.46 19588.30 12001.40 503.947


625 2478.62 96.988 221 102 112 113 99 11960 6277 5052 580 66610 55159 258.100615 2404.63 98.739 241 104 113 109 102 10277 7547 5601 467 93465 51520 205.600559 2413.13 103.708 290 102 116 109 105 10596 7377 7054 354 88519 24988 205.500660 2480.82 106.961 266 106 116 116 108 12785 7660 5293 255 88317 40925 211.100708 2826.67 110.265 268 107 122 106 105 11135 7374 5216 164 82991 50270 234.000

S0643 S0644 S0645 S0646 S0647 S0648 S0649 S0652 S0653 S0655 S0657 S0658 S0659

3718 1068 2163 303 2753 946 14.6200 0.86000 3.23000 42.1000 26.5000 2.64000 0.520003476 947 2184 303 2394 924 13.4200 2.09000 2.71000 45.1000 23.9000 2.37000 0.590003557 1307 2367 316 2742 1041 13.5900 2.06000 2.67000 44.1000 26.5000 2.42000 0.940005128 1363 2393 349 2585 1085 14.4500 2.05000 2.99000 43.2000 26.8000 2.66000 0.880005222 1279 2556 359 2581 1191 13.4100 2.08000 2.71000 45.7000 24.3000 3.32000 1.08000

S0661 S0662 S0663 S0664 S0665 S0667 S0668 S0669 S0670 S0671 S0672 S0673

2.65000 1.64000 6.0400 4.75000 77.5000 2.26000 0.60000 1.23000 0.74000 6.87000 2.92000 15462.35000 1.53000 10.0100 4.37000 77.3000 2.34000 0.90000 1.13000 1.41000 7.03000 3.29000 15672.51000 1.94000 10.6600 4.76000 74.8000 2.58000 1.07000 1.25000 1.49000 7.67000 3.46000 14872.59000 1.83000 10.2100 4.53000 69.3000 2.45000 1.09000 1.18000 1.45000 7.41000 3.47000 13632.44000 1.96000 11.1700 4.70000 77.9000 2.81000 1.14000 1.22000 1.55000 7.22000 3.06000 1320




S0674 S0675 S0676 S0678 S0680 S0681 S0683 S0684 S0687 S0688 S0690 S0692 S0694 S0695

584 117.100 17.9000 8.98000 45.2000 95.1000 41.2000 550 423 603 938 720 616 348616 126.900 20.9000 9.50000 44.3000 97.2000 35.8000 541 374 577 918 700 594 347628 120.500 23.8000 9.27000 40.7000 94.1000 39.3000 553 402 575 891 684 575 339713 128.700 28.1000 8.91000 44.2000 94.2000 41.7000 545 443 565 910 681 600 324687 133.700 24.4000 9.24000 47.6000 98.8000 38.0000 539 437 406 752 669 568 310

S0697 S0698 S0699 S0700 S0701 S0702 S0703 S0704 S0705 S0706 S0710 S0712 S0714

984 42.700 459 1451 485 305 19.4700 5.65000 8.14999 10.3600 38.2000 43.7000 9.28999980 96.800 465 1157 456 313 19.6300 5.65000 9.10000 10.2700 38.2000 41.6000 7.64000987 114.600 415 1501 452 286 18.0900 5.54000 6.16000 10.8200 38.3000 44.0000 6.63000

1000 121.800 451 1614 428 344 18.3000 5.71000 9.17000 12.1200 43.0000 42.4000 6.660001005 118.000 544 1577 422 329 17.5900 5.53000 9.41000 11.7800 42.7000 50.4000 7.95000

S0715 S0716 S0718 S0731 S0732 S0733 S0734 S0735 S0736 S0737 S0738 S0739 S0740

9.6000 6.42000 125 35.6000 34.1500 38.200 14.0100 17.6000 2.29000 5892 2433 2357 17179.3000 5.13000 727 39.7000 31.9600 74.400 14.3300 17.2000 4.31000 5618 2533 2572 1792

15.0000 6.07000 736 39.7000 28.7200 102.600 12.3100 23.5000 4.72000 5158 2341 2775 174615.1000 6.06000 391 38.0000 35.3200 99.900 13.7900 23.1000 4.34000 5030 2268 3012 183015.3000 6.30000 633 38.8000 33.2900 102.000 12.7700 25.4000 5.01000 4800 2288 3042 1828


16112 3362 978 1662 143.600 589 128.700 398 79.900 5675 12193 8806 16120 3149 30516749 3572 1318 1849 161.000 614 177.900 430 86.100 5725 11439 8860 16523 4305 33716885 3718 1522 1994 163.600 631 219.700 486 76.400 5217 12410 8322 15768 5110 39918241 4174 1637 2138 181.300 783 246.000 577 93.000 5310 13556 8012 15516 4797 32418945 4482 1768 2269 170.000 777 238.900 582 103.400 5745 11063 7892 14903 3991 351

S0756 S0757 S0758 S0759 S0760 S0761 S0762 S0763 S0765 S0766 S0767 S0768 S0769 S0770

29.5000 206 162.900 530 13871 251 15255 13991 5.23982 595.492 1894.90 101 613.840 207937.5000 203 177.300 534 13255 236 16047 15370 5.24898 558.273 1770.95 144 550.320 195238.9000 259 201.200 588 15013 249 15097 13445 5.26931 597.681 1911.58 112 636.880 194142.0000 335 252.100 745 12413 267 16079 15242 5.15463 567.031 1825.77 118 812.320 231141.2000 310 234.100 771 12752 257 14926 14992 5.15463 569.220 1844.84 132 780.720 3035


77 73 78 88 66 70 60 79 76 66 73 103 100 89 0.2800078 74 76 86 70 72 63 66 83 82 80 90 90 88 0.2800078 75 77 90 72 75 65 76 84 71 69 101 93 93 0.3300079 77 77 92 73 76 66 76 73 73 80 90 96 96 0.5000079 79 81 96 76 82 66 82 75 68 71 99 90 96 0.45000




S0798 S0799 S0800 S0801 S0802 S0803 S0811 S0822 S0825 S0826 S0835 S0840 S0841 S0842

105 75 83 85 65 105.100 104.900 54 83 77 96.730 400 3.80000 0.77000104 77 86 88 66 97.100 102.500 78 100 99 123.940 372 2.91000 2.9900098 77 86 87 67 95.800 107.900 77 90 98 111.000 431 2.96000 3.42000

105 78 89 90 68 103.400 116.400 76 94 79 112.880 425 2.71000 2.45000107 78 90 91 68 107.500 120.700 76 93 71 93.420 439 4.13000 4.05000


128 170 485 544 77 1557 131 2.87000 115.900 442 153 134 172 134 102.060126 102 528 558 81 1467 127 3.07000 116.000 368 128 126 118 105 100.780129 91 653 679 90 1653 131 2.97000 123.300 457 138 135 126 115 100.620130 109 577 562 89 1484 126 3.22000 123.000 656 138 140 126 118 98.210125 103 575 619 92 1507 127 2.88000 123.000 677 139 143 142 120 98.130

S0868 S0869 S0871 S0872 S0873 S0875 S0876 S0877 S0878 S0879 S0897 S0898 S0899 S0900

128 447.600 996 1275 82.6000 42 36 67 70 103 3667 99 203.300 339.900134 454.300 1148 1578 84.5000 46 40 73 68 106 2649 98 211.700 327.200133 458.700 1225 1477 84.6000 50 44 74 72 104 2544 97 210.900 333.500141 462.100 1188 1403 81.6000 48 45 79 73 106 3064 94 224.800 340.800134 443.000 1492 1982 84.2000 50 48 84 79 107 3063 94 221.400 369.200

S0901 S0903 S0904 S0905 S0906 S0907 S0908 S0909 S0910 S0911 S0912 S0913 S0914

2.19800 97 96 97 102 102 101 80.8000 86 86.4000 904.00 2.62000 34.60002.30800 97 97 98 102 102 107 82.5000 87 89.6000 914.70 3.08000 36.55002.54600 98 99 101 101 101 110 83.5000 87 87.3000 926.40 3.06000 36.45002.95500 102 100 101 103 103 98 81.5000 93 79.8000 985.50 3.12000 35.04002.75400 100 102 104 103 103 101 84.3000 92 85.6000 1010.00 3.54000 39.1100

S0927 S0931 S0932 S0933 S0934 S0936 S0937 S0938 S0939 S0940 S0952 S0953 S0954

3.37417 14959 23.3700 14.6000 11.7000 309 27.2100 24.1000 31.1700 27.0600 0.6070 96326 807193.08649 18418 21.0200 11.7000 9.2000 280 33.8300 31.3200 14.6400 12.8100 1.0470 106430 812832.97041 19865 24.3900 11.0000 8.6000 263 38.7400 35.9600 14.9000 13.3100 3.3580 101585 816902.68785 40349 23.7900 10.6000 8.2000 244 29.4600 26.4000 16.3600 14.2000 7.3920 117653 819601.88707 16985 25.7500 10.0000 7.7000 247 27.9500 25.0500 18.1200 16.1800 15.5450 128520 81741

S0955 S0956 S0957 S0958 S0978 S0980 S0981 S0982 S0983 S0984 S0985 S0986 S0990 S0991 S0992 S0993

77313 2869 76324 5447 693 667 531 156 60 26962 159 4682 3318 8989 1170 627977489 2909 76458 5412 677 653 540 154 55 29191 194 4917 3109 10498 1688 837177957 3094 77101 5215 477 591 496 181 59 10450 23 4711 3795 8898 1790 1196378408 3287 77339 4697 570 568 472 198 106 45958 182 6542 3333 8799 2415 1449078357 3531 77692 4344 545 518 438 210 71 32069 160 10323 3287 8355 1679 12875




S0994 S0995 S0997 S0998 S0999 S1001

23104 1107 3928 33 26.0000 4530479 1179 4830 28 24.5000 2832670 1706 4926 40 27.9000 3738568 1802 6465 31 29.1000 2957017 2222 8588 36 34.7000 15


Sashelp.mon111 — M-Competition 111 Series, MonthlyThe Sashelp.mon111 data set provides m-competition 111 series, monthly. The following steps display infor-mation about the data set Sashelp.mon111 and create Figure 1.62. The data set contains 124 observations.

title "Sashelp.mon111 --- M-Competition 111 Series, Monthly";proc contents data=sashelp.mon111 varnum;


title "The First Five Observations Out of 124";proc print data=sashelp.mon111(obs=5) heading=h noobs;run;


Figure 1.62 Sashelp.mon111 — M-Competition 111 Series, Monthly




# Variable Type Len

1 T Num 42 S391 Num 53 S409 Num 54 S418 Num 55 S427 Num 56 S436 Num 57 S445 Num 58 S454 Num 59 S463 Num 5





T S391 S409 S418 S427 S436 S445 S454 S463 S472 S481 S490 S499 S508

1 117497 443 403 13224.00 2107 90000 2783.70 12344.00 2496.26 24 292.900 780 138582 153276 469 372 14404.20 2097 87000 3746.94 13970.70 2485.05 77 297.900 815 149193 241443 251 381 17824.00 2135 111000 6404.38 13587.90 2639.59 54 351.400 929 154514 218709 286 465 20295.00 2165 108000 5074.96 13109.50 2649.48 43 286.500 851 147605 202896 342 402 19384.00 2205 77000 4526.14 12152.59 2313.73 14 293.500 921 16263

S526 S544 S562 S571 S580 S589 S598 S607 S616 S625 S634 S643 S652

1759.81 151.511 7.50000 23746.89 12480.70 3986 140 90 1081.76 2478.62 5052 3718 0.860001912.56 148.884 6.40000 23746.89 12285.00 4175 134 102 1080.11 2404.63 5601 3476 2.090002171.54 148.192 7.00000 27454.09 12325.00 4175 144 99 1353.93 2413.13 7054 3557 2.060002191.08 144.459 6.70000 23829.30 12269.40 4500 140 108 1326.56 2480.82 5293 5128 2.050001804.54 146.740 5.50000 24712.59 12327.20 4625 139 109 1406.02 2826.67 5216 5222 2.08000

S661 S670 S688 S697 S706 S715 S733 S742 S751 S760 S769 S787 S796

2.65000 0.74000 603 984 10.3600 9.6000 38.200 3362 12193 13871 613.840 60 0.280002.35000 1.41000 577 980 10.2700 9.3000 74.400 3572 11439 13255 550.320 63 0.280002.51000 1.49000 575 987 10.8200 15.0000 102.600 3718 12410 15013 636.880 65 0.330002.59000 1.45000 565 1000 12.1200 15.1000 99.900 4174 13556 12413 812.320 66 0.500002.44000 1.55000 406 1005 11.7800 15.3000 102.000 4482 11063 12752 780.720 66 0.45000

S841 S850 S859 S868 S877 S904 S913 S931 S940 S958 S985 S994

3.80000 170 2.87000 128 67 96 2.62000 14959 27.0600 5447 159 231042.91000 102 3.07000 134 73 97 3.08000 18418 12.8100 5412 194 304792.96000 91 2.97000 133 74 99 3.06000 19865 13.3100 5215 23 326702.71000 109 3.22000 141 79 100 3.12000 40349 14.2000 4697 182 385684.13000 103 2.88000 134 84 102 3.54000 16985 16.1800 4344 160 57017

Sashelp.mwelect — Midwest Electrical Supply Monthly Sales By Productgroup F 109

Sashelp.mwelect — Midwest Electrical Supply Monthly SalesBy ProductgroupThe Sashelp.mwelect data set provides midwest electrical supply monthly sales by product group. Thefollowing steps display information about the data set Sashelp.mwelect and create Figure 1.63. The data setcontains 11,296 observations.

title "Sashelp.mwelect --- Midwest Electrical Supply Monthly Sales By Productgroup";proc contents data=sashelp.mwelect varnum;


title "The First Five Observations Out of 11296";proc print data=sashelp.mwelect(obs=5) heading=h noobs;run;

Figure 1.63 Sashelp.mwelect — Midwest Electrical Supply Monthly Sales By Productgroup

Sashelp.mwelect --- Midwest Electrical Supply Monthly Sales By Productgroup




1 Date Num 8 MONYY7. Month2 ProductGroup Char 14 Product Group3 SalesRegion Char 7 Sales Region4 SalesOffice Char 11 Sales Office5 SalesInUsd Num 8 COMMA10.2 Sales in USD6 SalesCost Num 8 COMMA9.2 Cost of Goods Sold7 QuantityInvoiced Num 8 7. Units Sold8 SKU Char 18 $CHAR18. Stock-keeping unit


Product Sales Sales QuantityDate Group Region Office SalesInUsd SalesCost Invoiced SKU

JAN2001 Electrical East Buffalo 975.18 1,015.81 432 CPR 00108N 0400RIFEB2001 Electrical East Buffalo 324.20 341.20 432 CPR 00108N 0400RIMAR2001 Electrical East Buffalo 1,948.74 2,047.14 . CPR 00108N 0400RIAPR2001 Electrical East Buffalo . . . CPR 00108N 0400RIMAY2001 Electrical East Buffalo 342.12 348.40 432 CPR 00108N 0400RI


Sashelp.nvst1The Sashelp.nvst1 data set provides investor transaction data. The following steps display information aboutthe data set Sashelp.nvst1 and create Figure 1.64. The data set contains 6 observations.

title "Sashelp.nvst1";proc contents data=sashelp.nvst1 varnum;


title "The First Five Observations Out of 6";proc print data=sashelp.nvst1(obs=5) heading=h noobs;run;

Figure 1.64 Sashelp.nvst1

Sashelp.nvst1






DATE AMOUNT

01JAN1997 -3000001JAN1998 750001JAN1999 750001JAN2000 750001JAN2001 7500

Sashelp.nvst2 F 111






Sashelp.nvst2






DATE AMOUNT








Sashelp.nvst3






DATE AMOUNT


Sashelp.nvst4 F 113






Sashelp.nvst4






DATE AMOUNT








Sashelp.nvst5






DATE AMOUNT


Sashelp.orsales — Orion Star Sports & Outdoors Sales 1999 - 2002 F 115

Sashelp.orsales — Orion Star Sports & Outdoors Sales 1999 -2002The Sashelp.orsales data set provides Orion star sports and outdoors sales 1999–2002. The following stepsdisplay information about the data set Sashelp.orsales and create Figure 1.69. The data set contains 912observations.

title "Sashelp.orsales --- Orion Star Sports & Outdoors Sales 1999 - 2002";proc contents data=sashelp.orsales varnum;


title "The First Five Observations Out of 912";proc print data=sashelp.orsales(obs=5) heading=h noobs;run;

title "The Product_Line Variable";proc freq data=sashelp.orsales;

tables Product_Line;run;

Figure 1.69 Sashelp.orsales — Orion Star Sports & Outdoors Sales 1999 - 2002

Sashelp.orsales --- Orion Star Sports & Outdoors Sales 1999 - 2002




1 Year Num 8 4. Year2 Quarter Char 6 Quarter3 Product_Line Char 20 Product Line4 Product_Category Char 25 Product Category5 Product_Group Char 25 Product Group6 Quantity Num 8 6. Number of Items7 Profit Num 8 12.2 Profit in USD8 Total_Retail_Price Num 8 12.2 Total Retail Price in USD


Product_ Product_ Total_Year Quarter Line Category Product_Group Quantity Profit Retail_Price

1999 1999Q1 Children Children Sports A-Team, Kids 286 4980.15 8990.901999 1999Q1 Children Children Sports Bathing Suits, Kids 98 1479.95 2560.401999 1999Q1 Children Children Sports Eclipse, Kid's Clothes 588 9348.95 18768.801999 1999Q1 Children Children Sports Eclipse, Kid's Shoes 334 7136.80 14337.201999 1999Q1 Children Children Sports Lucky Guy, Kids 303 7163.00 12996.20



The Product_Line Variable

The FREQ Procedure

Product Line

Cumulative CumulativeProduct_Line Frequency Percent Frequency Percent--------------------------------------------------------------------Children 176 19.30 176 19.30Clothes & Shoes 288 31.58 464 50.88Outdoors 112 12.28 576 63.16Sports 336 36.84 912 100.00

Sashelp.plfips — FIPS Place Codes from USGS Geographic Names Information System (GNIS) F 117

Sashelp.plfips — FIPS Place Codes from USGS GeographicNames Information System (GNIS)The Sashelp.plfips data set provides FIPS place codes from USGS geographic names information system(GNIS). The following steps display information about the data set Sashelp.plfips and create Figure 1.70.The data set contains 173,283 observations.

title "Sashelp.plfips --- FIPS Place Codes from USGS Geographic Names Information"" System (GNIS)";

proc contents data=sashelp.plfips varnum;ods select position;

run;

title "The First Five Observations Out of 173283";proc print data=sashelp.plfips(obs=5) heading=h noobs;run;

title "The FEATURE_CLASS Variable";proc freq data=sashelp.plfips;

tables FEATURE_CLASS;run;

Figure 1.70 Sashelp.plfips — FIPS Place Codes from USGS Geographic Names Information System(GNIS)

Sashelp.plfips --- FIPS Place Codes from USGS Geographic Names Information System (GNIS)




1 FEATURE_ID Char 9 $9. $9. USGS GNIS Unique Identifier2 FEATURE_CLASS Char 15 $15. $15. Description (USGS GNIS Feature Class)3 CENSUS_CLASS_ Char 2 $2. $2. FIPS Place Class Code (USGSCODE GNIS Census Class Code)

4 NAME Char 52 $CHAR52. Place name (USGS GNIS Feature Name5 NAME2 Char 52 $CHAR52. Place name (Normalized Feature Name)6 STATE Char 2 $CHAR2.7 PLACE Num 8 Z5. FIPS Place Code (USGS GNIS Census Code)




CENSUS_FEATURE_ CLASS_ID FEATURE_CLASS CODE NAME

1418109 Populated Place P1 ADAK1397926 Populated Place U6 AFOGNAK1397967 Populated Place U6 AGUIKCHUK2419295 Civil E7 AHTNA ALASKA NATIVE REGIONAL CORPORATION1398007 Populated Place P1 AKHIOK

NAME2 STATE PLACE

ADAK AK 00065AFOGNAK AK 00425AGUIKCHUK AK 00540AHTNAALASKANATIVEREGIONALCORPORATION AK 00590AKHIOK AK 00650

The FEATURE_CLASS Variable

The FREQ Procedure

Description (USGS GNIS Feature Class)

Cumulative CumulativeFEATURE_CLASS Frequency Percent Frequency Percent--------------------------------------------------------------------Census 9661 5.58 9661 5.58Civil 40724 23.50 50385 29.08Locale 974 0.56 51359 29.64Military 32 0.02 51391 29.66Park 7 0.00 51398 29.66Populated Place 121872 70.33 173270 99.99Post Office 13 0.01 173283 100.00

Sashelp.port_multi F 119

Sashelp.port_multiThe following steps display information about the data set Sashelp.port_multi and create Figure 1.71. Thedata set contains 915 observations.

title "Sashelp.port_multi";proc contents data=sashelp.port_multi varnum;


title "The First Five Observations Out of 915";proc print data=sashelp.port_multi(obs=5) heading=h noobs;run;

Figure 1.71 Sashelp.port_multi

Sashelp.port_multi






Term

a fim dea fim de quea menos quea par daa par das

Role

AdjAdjAdjAdjAdj


Sashelp.prdsal2 — Furniture Sales DataThe Sashelp.prdsal2 data set provides furniture sales data. The following steps display information about thedata set Sashelp.prdsal2 and create Figure 1.72. The data set contains 23,040 observations.

title "Sashelp.prdsal2 --- Furniture Sales Data";proc contents data=sashelp.prdsal2 varnum;


title "The First Five Observations Out of 23040";proc print data=sashelp.prdsal2(obs=5) heading=h noobs;run;

title "The PRODTYPE Variable";proc freq data=sashelp.prdsal2;

tables PRODTYPE;run;

Figure 1.72 Sashelp.prdsal2 — Furniture Sales Data

Sashelp.prdsal2 --- Furniture Sales Data




1 COUNTRY Char 10 $CHAR10. Country2 STATE Char 22 $CHAR22. State/Province3 COUNTY Char 20 $CHAR20. County4 ACTUAL Num 8 DOLLAR12.2 Actual Sales5 PREDICT Num 8 DOLLAR12.2 Predicted Sales6 PRODTYPE Char 10 $CHAR10. Product Type7 PRODUCT Char 10 $CHAR10. Product8 YEAR Num 8 4. Year9 QUARTER Num 8 8. Quarter

10 MONTH Num 8 MONNAME3. Month11 MONYR Num 8 MONYY. MONYY. Month/Year

Sashelp.prdsal2 — Furniture Sales Data F 121



COUNTRY STATE COUNTY ACTUAL

U.S.A. California $987.36U.S.A. California $1,782.96U.S.A. California $32.64U.S.A. California $1,825.12U.S.A. California $750.72

PREDICT PRODTYPE PRODUCT YEAR QUARTER MONTH MONYR

$692.24 FURNITURE SOFA 1995 1 Jan JAN95$568.48 FURNITURE SOFA 1995 1 Feb FEB95$16.32 FURNITURE SOFA 1995 1 Mar MAR95

$756.16 FURNITURE SOFA 1995 2 Apr APR95$723.52 FURNITURE SOFA 1995 2 May MAY95

The PRODTYPE Variable

The FREQ Procedure

Product Type

Cumulative CumulativePRODTYPE Frequency Percent Frequency Percent---------------------------------------------------------------FURNITURE 11520 50.00 11520 50.00OFFICE 11520 50.00 23040 100.00


Sashelp.prdsal3 — Furniture Sales DataThe Sashelp.prdsal3 data set provides furniture sales data. The following steps display information about thedata set Sashelp.prdsal3 and create Figure 1.73. The data set contains 11,520 observations.

title "Sashelp.prdsal3 --- Furniture Sales Data";proc contents data=sashelp.prdsal3 varnum;


title "The First Five Observations Out of 11520";proc print data=sashelp.prdsal3(obs=5) heading=h noobs;run;

title "The PRODTYPE Variable";proc freq data=sashelp.prdsal3;

tables PRODTYPE;run;

Figure 1.73 Sashelp.prdsal3 — Furniture Sales Data

Sashelp.prdsal3 --- Furniture Sales Data




1 COUNTRY Char 10 $CHAR10. Country2 STATE Char 22 $CHAR22. State/Province3 COUNTY Char 20 $CHAR20. County4 ACTUAL Num 8 DOLLAR12.2 Actual Sales5 PREDICT Num 8 DOLLAR12.2 Predicted Sales6 PRODTYPE Char 10 $CHAR10. Product Type7 PRODUCT Char 10 $CHAR10. Product8 YEAR Num 8 4. Year9 QUARTER Num 8 8. Quarter

10 MONTH Num 8 MONNAME3. Month11 DATE Num 8 MONYY. MONYY. Date

Sashelp.prdsal3 — Furniture Sales Data F 123



COUNTRY STATE COUNTY ACTUAL

U.S.A. California $726.00U.S.A. California $1,311.00U.S.A. California $24.00U.S.A. California $1,342.00U.S.A. California $552.00

PREDICT PRODTYPE PRODUCT YEAR QUARTER MONTH DATE

$509.00 FURNITURE SOFA 1997 1 Jan JAN97$418.00 FURNITURE SOFA 1997 1 Feb FEB97$12.00 FURNITURE SOFA 1997 1 Mar MAR97

$556.00 FURNITURE SOFA 1997 2 Apr APR97$532.00 FURNITURE SOFA 1997 2 May MAY97

The PRODTYPE Variable

The FREQ Procedure

Product Type



Sashelp.prdsale — Furniture Sales DataThe Sashelp.prdsale data set provides furniture sales data. The following steps display information about thedata set Sashelp.prdsale and create Figure 1.74. The data set contains 1,440 observations.

title "Sashelp.prdsale --- Furniture Sales Data";proc contents data=sashelp.prdsale varnum;


title "The First Five Observations Out of 1440";proc print data=sashelp.prdsale(obs=5) heading=h noobs;run;

title "The DIVISION and PRODTYPE Variables";proc freq data=sashelp.prdsale;

tables DIVISION;tables PRODTYPE;

run;

Figure 1.74 Sashelp.prdsale — Furniture Sales Data

Sashelp.prdsale --- Furniture Sales Data




1 ACTUAL Num 8 DOLLAR12.2 Actual Sales2 PREDICT Num 8 DOLLAR12.2 Predicted Sales3 COUNTRY Char 10 $CHAR10. Country4 REGION Char 10 $CHAR10. Region5 DIVISION Char 10 $CHAR10. Division6 PRODTYPE Char 10 $CHAR10. Product type7 PRODUCT Char 10 $CHAR10. Product8 QUARTER Num 8 8. Quarter9 YEAR Num 8 4. Year

10 MONTH Num 8 MONNAME3. Month

Sashelp.prdsale — Furniture Sales Data F 125



ACTUAL PREDICT COUNTRY REGION DIVISION

$925.00 $850.00 CANADA EAST EDUCATION$999.00 $297.00 CANADA EAST EDUCATION$608.00 $846.00 CANADA EAST EDUCATION$642.00 $533.00 CANADA EAST EDUCATION$656.00 $646.00 CANADA EAST EDUCATION

PRODTYPE PRODUCT QUARTER YEAR MONTH

FURNITURE SOFA 1 1993 JanFURNITURE SOFA 1 1993 FebFURNITURE SOFA 1 1993 MarFURNITURE SOFA 2 1993 AprFURNITURE SOFA 2 1993 May

The DIVISION and PRODTYPE Variables

The FREQ Procedure

Division

Cumulative CumulativeDIVISION Frequency Percent Frequency Percent---------------------------------------------------------------CONSUMER 720 50.00 720 50.00EDUCATION 720 50.00 1440 100.00

Product type



Sashelp.pricedata — Simulated Monthly Sales Data WithHierarchy of Region, Line, ProductThe Sashelp.pricedata data set provides simulated monthly sales data with hierarchy of region, line, product.The following steps display information about the data set Sashelp.pricedata and create Figure 1.75. Thedata set contains 1,020 observations.

title "Sashelp.pricedata --- Simulated Monthly Sales Data With Hierarchy of Region,"" Line, Product";

proc contents data=sashelp.pricedata varnum;ods select position;

run;

title "The First Five Observations Out of 1020";proc print data=sashelp.pricedata(obs=5) heading=h noobs;run;

title "The regionName and productLine Variables";proc freq data=sashelp.pricedata;

tables regionName;tables productLine;

run;

Sashelp.pricedata — Simulated Monthly Sales Data With Hierarchy of Region, Line, Product F 127

Figure 1.75 Sashelp.pricedata — Simulated Monthly Sales Data With Hierarchy of Region, Line, Product

Sashelp.pricedata --- Simulated Monthly Sales Data With Hierarchy of Region, Line, Product




1 date Num 8 MONYY. Order Date2 sale Num 8 Unit Sale3 price Num 8 Unit Price4 discount Num 8 Price Discount5 cost Num 8 Unit Cost6 price1 Num 8 Product 1 Unit Price7 price2 Num 8 Product 2 Unit Price8 price3 Num 8 Product 3 Unit Price9 price4 Num 8 Product 4 Unit Price

10 price5 Num 8 Product 5 Unit Price11 price6 Num 8 Product 6 Unit Price12 price7 Num 8 Product 7 Unit Price13 price8 Num 8 Product 8 Unit Price14 price9 Num 8 Product 9 Unit Price15 price10 Num 8 Product 10 Unit Price16 price11 Num 8 Product 11 Unit Price17 price12 Num 8 Product 12 Unit Price18 price13 Num 8 Product 13 Unit Price19 price14 Num 8 Product 14 Unit Price20 price15 Num 8 Product 15 Unit Price21 price16 Num 8 Product 16 Unit Price22 price17 Num 8 Product 17 Unit Price23 regionName Char 7 Sales Region24 productLine Char 5 Name of product line25 productName Char 9 Product Name26 region Num 8 6. Region ID27 line Num 8 6. Product Line ID28 product Num 8 6. Product ID




date sale price discount cost price1 price2 price3 price4 price5 price6

JAN98 355 52.300 0.00 23.9 52.300 115 33.40 67.9 36 38.88FEB98 398 52.300 0.00 23.9 52.300 115 33.40 67.9 36 48.60MAR98 387 52.300 0.00 23.9 52.300 115 33.40 67.9 36 48.60APR98 380 52.300 0.00 23.9 52.300 115 28.39 67.9 36 48.60MAY98 555 44.455 0.15 23.9 44.455 115 33.40 67.9 36 48.60

price7 price8 price9 price10 price11 price12 price13 price14 price15

42 59 65.2 56.900 171.40 147 122 53.00 120.242 59 65.2 56.900 171.40 147 122 53.00 120.242 59 65.2 48.365 154.26 147 122 53.00 120.242 59 65.2 56.900 171.40 147 122 45.05 120.242 59 65.2 56.900 171.40 147 122 53.00 120.2

region product productprice16 price17 Name Line Name region line product

70.55 80.5 Region1 Line1 Product1 1 1 183.00 80.5 Region1 Line1 Product1 1 1 183.00 80.5 Region1 Line1 Product1 1 1 183.00 80.5 Region1 Line1 Product1 1 1 183.00 80.5 Region1 Line1 Product1 1 1 1

The regionName and productLine Variables

The FREQ Procedure

Sales Region

region Cumulative CumulativeName Frequency Percent Frequency Percent------------------------------------------------------------Region1 180 17.65 180 17.65Region2 480 47.06 660 64.71Region3 360 35.29 1020 100.00

Name of product line

product Cumulative CumulativeLine Frequency Percent Frequency Percent------------------------------------------------------------Line1 180 17.65 180 17.65Line2 240 23.53 420 41.18Line3 240 23.53 660 64.71Line4 240 23.53 900 88.24Line5 120 11.76 1020 100.00

Sashelp.proj4def F 129

Sashelp.proj4defThe Sashelp.proj4def data set provides ESPG and ESRI map projection grids. The following steps displayinformation about the data set Sashelp.proj4def and create Figure 1.76. The data set contains 7,124observations.

title "Sashelp.proj4def";proc contents data=sashelp.proj4def varnum;


title "The First Five Observations Out of 7124";proc print data=sashelp.proj4def(obs=5) heading=h noobs;run;

Figure 1.76 Sashelp.proj4def

Sashelp.proj4def




1 NAME Char 13 $13. $13. Name2 STRING Char 236 $236. $236. Projection String3 DESC Char 73 $73. $73. Description




NAME

EPSG:3819EPSG:3821EPSG:3824EPSG:3889EPSG:3906

STRING

+proj=longlat +ellps=bessel +towgs84=595.48,121.69,515.35,4.115,-2.9383,0.853,-3.408 +no_defs+proj=longlat +ellps=aust_SA +no_defs+proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs+proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs+proj=longlat +ellps=bessel +towgs84=682,-203,480,0,0,0,0 +no_defs

DESC

HD1909TWD67TWD97IGRSMGI 1901

Sashelp.qtr1001 — M-Competition 1001 Series, Quarterly F 131

Sashelp.qtr1001 — M-Competition 1001 Series, QuarterlyThe Sashelp.qtr1001 data set provides m-competition 1001 series, quarterly. The following steps displayinformation about the data set Sashelp.qtr1001 and create Figure 1.77. The data set contains 101 observations.

title "Sashelp.qtr1001 --- M-Competition 1001 Series, Quarterly";proc contents data=sashelp.qtr1001 varnum;


title "The First Five Observations Out of 101";proc print data=sashelp.qtr1001(obs=5) heading=h noobs;run;


Figure 1.77 Sashelp.qtr1001 — M-Competition 1001 Series, Quarterly

Sashelp.qtr1001 --- M-Competition 1001 Series, Quarterly



# Variable Type Len








# Variable Type Len

53 S0322 Num 554 S0323 Num 555 S0324 Num 556 S0325 Num 557 S0326 Num 558 S0328 Num 559 S0329 Num 560 S0330 Num 561 S0332 Num 562 S0333 Num 563 S0335 Num 564 S0336 Num 565 S0337 Num 566 S0338 Num 567 S0344 Num 568 S0345 Num 569 S0351 Num 570 S0352 Num 571 S0360 Num 572 S0361 Num 573 S0363 Num 574 S0364 Num 575 S0365 Num 576 S0366 Num 577 S0367 Num 578 S0368 Num 579 S0369 Num 580 S0370 Num 581 S0371 Num 582 S0374 Num 583 S0375 Num 584 S0376 Num 585 S0378 Num 586 S0380 Num 587 S0381 Num 588 S0382 Num 589 S0383 Num 590 S0384 Num 5




T S0184 S0185 S0188 S0189 S0191 S0192 S0195 S0196 S0199 S0205 S0206 S0207 S0212

1 94 9366.37 13.3000 30.1888 5666 29 17050 65896 68462 1299 960 960 342 132 9169.55 33.9000 38.5167 5405 37 18408 63922 63023 1212 923 923 923 131 7776.84 19.6000 43.7217 5001 42 18540 76160 71918 1031 777 777 764 127 11186.30 14.2000 46.8446 4688 45 20505 77622 81403 1118 956 956 855 77 11710.70 18.4000 36.4347 5492 35 18523 78125 80790 1248 1140 1140 77

S0213 S0217 S0219 S0224 S0225 S0226 S0228 S0229 S0230 S0231 S0233 S0234 S0235 S0236

290.460 2334 2310 1440 41 31 52 82 77 14.7000 7 14 34 59292.330 2598 4190 1436 41 40 67 81 88 15.0000 7 15 35 63262.370 3067 10735 1358 38 40 61 66 64 11.9000 7 15 30 55299.720 3318 4009 1525 44 44 64 85 90 15.7000 8 14 32 66286.560 2710 4134 1559 45 32 61 82 71 15.3000 8 12 26 52

S0237 S0238 S0240 S0266 S0267 S0270 S0276 S0277 S0278 S0280 S0285 S0286 S0287

309 65.1000 585.641 81 48 72 23.6000 36.8000 157.800 133.700 77 100 64341 65.9000 590.403 91 51 76 30.4000 50.3000 209.100 194.400 78 100 55339 65.8000 495.177 100 47 65 29.0000 47.0000 193.100 186.900 76 97 53380 73.6000 616.590 92 51 80 27.8000 50.1000 211.100 189.900 91 108 60386 65.0000 602.306 87 50 78 30.6000 43.9000 191.700 184.100 91 106 75

S0289 S0290 S0292 S0293 S0296 S0297 S0301 S0307 S0308 S0317 S0318 S0322 S0323 S0324

81 682 108 1026 2892 65 63 7.09000 63 160 83.8000 81 55 7179 673 103 1134 3206 71 63 8.46000 67 161 90.7000 82 58 7481 667 102 1078 2867 70 61 8.62000 70 147 80.6000 65 50 7386 786 130 1226 3446 74 60 9.38000 71 139 84.3000 83 60 7286 793 87 1087 3320 69 58 8.06000 69 149 92.7000 83 61 70

S0325 S0326 S0328 S0329 S0330 S0332 S0333 S0335 S0336 S0337 S0338 S0344

64 85 83.8000 81.8000 82.0000 82.9000 84.4000 2.66000 60 54.1000 63 7763 73 90.7000 85.0000 84.7000 84.8000 90.7000 3.66000 62 59.5000 67 7861 58 80.6000 86.8000 86.0000 86.5000 81.1000 3.27000 60 56.5000 70 6560 57 84.3000 86.4000 85.6000 86.3000 84.7000 2.58000 64 63.9000 71 8458 59 92.7000 86.1000 88.4000 88.5000 91.0000 2.49000 60 57.8000 69 84

S0345 S0351 S0352 S0360 S0361 S0363 S0364 S0365 S0366 S0367 S0368 S0369

75 99.000 94.700 16.3100 4.56000 3.50000 6903 1.03000 82 43 313 37.200074 99.400 96.900 16.7200 3.56000 1.80000 7047 1.04000 85 63 321 15.700061 100.600 100.600 16.8400 3.38000 1.80000 7162 1.07000 33 74 325 11.000080 100.600 102.800 16.8300 3.46000 1.90000 7250 1.06000 44 64 324 26.600083 99.400 104.100 16.9400 4.06000 2.40000 7269 1.07000 47 70 325 28.9000

S0370 S0371 S0374 S0375 S0376 S0378 S0380 S0381 S0382 S0383 S0384

2.10000 29.0000 102 561 87.3000 197 1184 71.0000 40.8000 659 6.420003.90000 30.1000 102 373 90.1000 348 1196 50.9000 36.9000 660 6.160002.90000 30.6000 100 282 92.9000 360 1209 40.0000 44.2000 661 6.300002.50000 31.3000 104 346 91.5000 668 1216 43.0000 62.1000 673 7.580003.20000 33.2000 100 546 91.5000 630 1242 51.8410 57.1000 686 7.87000


Sashelp.qtr111 — M-Competition 111 Series, QuarterlyThe Sashelp.qtr111 data set provides m-competition 111 series, quarterly. The following steps displayinformation about the data set Sashelp.qtr111 and create Figure 1.78. The data set contains 57 observations.

title "Sashelp.qtr111 --- M-Competition 111 Series, Quarterly";proc contents data=sashelp.qtr111 varnum;


title "The First Five Observations Out of 57";proc print data=sashelp.qtr111(obs=5) heading=h noobs;run;

Figure 1.78 Sashelp.qtr111 — M-Competition 111 Series, Quarterly




# Variable Type Len


10 S382 Num 5


T S184 S229 S238 S292 S301 S328 S337 S364 S382

1 94 82 65.1000 108 63 83.8000 54.1000 6903 40.80002 132 81 65.9000 103 63 90.7000 59.5000 7047 36.90003 131 66 65.8000 102 61 80.6000 56.5000 7162 44.20004 127 85 73.6000 130 60 84.3000 63.9000 7250 62.10005 77 82 65.0000 87 58 92.7000 57.8000 7269 57.1000


Sashelp.rentThe Sashelp.rent data set provides a data set with two variables, date and amount. The following steps displayinformation about the data set Sashelp.rent and create Figure 1.79. The data set contains 10 observations.

title "Sashelp.rent";proc contents data=sashelp.rent varnum;


title "The First Five Observations Out of 10";proc print data=sashelp.rent(obs=5) heading=h noobs;run;

Figure 1.79 Sashelp.rent

Sashelp.rent






DATE AMOUNT


Sashelp.retail — Retail Sales (Quarterly: 1980q1-1994q2) F 137

Sashelp.retail — Retail Sales (Quarterly: 1980q1-1994q2)The Sashelp.retail data set provides retail sales (quarterly: 1980q1–1994q2). The following steps displayinformation about the data set Sashelp.retail and create Figure 1.80. The data set contains 58 observations.

title "Sashelp.retail --- Retail Sales (Quarterly: 1980q1-1994q2)";proc contents data=sashelp.retail varnum;


title "The First Five Observations Out of 58";proc print data=sashelp.retail(obs=5) heading=h noobs;run;

Figure 1.80 Sashelp.retail — Retail Sales (Quarterly: 1980q1-1994q2)

Sashelp.retail --- Retail Sales (Quarterly: 1980q1-1994q2)




1 SALES Num 8 DOLLAR10. Retail sales in millions of $2 DATE Num 8 YYQ4.3 YEAR Num 84 MONTH Num 85 DAY Num 8


SALES DATE YEAR MONTH DAY

$220 80Q1 1980 1 1$257 80Q2 1980 4 1$258 80Q3 1980 7 1$295 80Q4 1980 10 1$247 81Q1 1981 1 1


Sashelp.revhub2 — Airline DataThe Sashelp.revhub2 data set provides airline data. The following steps display information about the dataset Sashelp.revhub2 and create Figure 1.81. The data set contains 72 observations.

title "Sashelp.revhub2 --- Airline Data";proc contents data=sashelp.revhub2 varnum;


title "The First Five Observations Out of 72";proc print data=sashelp.revhub2(obs=5) heading=h noobs;run;

title "The HUB and SOURCE Variables";proc freq data=sashelp.revhub2;

tables HUB;tables SOURCE;

run;

Figure 1.81 Sashelp.revhub2 — Airline Data

Sashelp.revhub2 --- Airline Data




1 HUB Char 16 HUB2 SOURCE Char 16 REVENUE SOURCE3 TYPE Char 16 TYPE4 REVENUE Num 8 DOLLAR15. REVENUE


HUB SOURCE TYPE REVENUE

Frankfurt Freight Direct $1,464,938Frankfurt Freight Indirect $198,942Frankfurt Freight Other $144,685Frankfurt Other Direct $111,193Frankfurt Other Indirect $12,057

Sashelp.revhub2 — Airline Data F 139


The HUB and SOURCE Variables

The FREQ Procedure

HUB

Cumulative CumulativeHUB Frequency Percent Frequency Percent------------------------------------------------------------------Frankfurt 12 16.67 12 16.67London 12 16.67 24 33.33New York 12 16.67 36 50.00San Francisco 12 16.67 48 66.67Sydney 12 16.67 60 83.33Tokyo 12 16.67 72 100.00

REVENUE SOURCE

Cumulative CumulativeSOURCE Frequency Percent Frequency Percent--------------------------------------------------------------Freight 18 25.00 18 25.00Other 18 25.00 36 50.00Passenger 18 25.00 54 75.00Service 18 25.00 72 100.00


Sashelp.rockpitThe Sashelp.rockpit data set provides sample data for a SAS/ETS example, performing time value analysis.The following steps display information about the data set Sashelp.rockpit and create Figure 1.82. The dataset contains 6 observations.

title "Sashelp.rockpit";proc contents data=sashelp.rockpit varnum;


title "The First Five Observations Out of 6";proc print data=sashelp.rockpit(obs=5) heading=h noobs;run;

Figure 1.82 Sashelp.rockpit

Sashelp.rockpit






DATE AMOUNT


Sashelp.shoes — Fictitious Shoe Company Data F 141

Sashelp.shoes — Fictitious Shoe Company DataThe Sashelp.shoes data set provides fictitious shoe company data. The following steps display informationabout the data set Sashelp.shoes and create Figure 1.83. The data set contains 395 observations.

title "Sashelp.shoes --- Fictitious Shoe Company Data";proc contents data=sashelp.shoes varnum;


title "The First Five Observations Out of 395";proc print data=sashelp.shoes(obs=5) heading=h noobs;run;

title "The Product Variable";proc freq data=sashelp.shoes;

tables Product;run;

Figure 1.83 Sashelp.shoes — Fictitious Shoe Company Data

Sashelp.shoes --- Fictitious Shoe Company Data




1 Region Char 252 Product Char 143 Subsidiary Char 124 Stores Num 8 Number of Stores5 Sales Num 8 DOLLAR12. DOLLAR12. Total Sales6 Inventory Num 8 DOLLAR12. DOLLAR12. Total Inventory7 Returns Num 8 DOLLAR12. DOLLAR12. Total Returns


Region Product Subsidiary Stores Sales Inventory Returns

Africa Boot Addis Ababa 12 $29,761 $191,821 $769Africa Men's Casual Addis Ababa 4 $67,242 $118,036 $2,284Africa Men's Dress Addis Ababa 7 $76,793 $136,273 $2,433Africa Sandal Addis Ababa 10 $62,819 $204,284 $1,861Africa Slipper Addis Ababa 14 $68,641 $279,795 $1,771



The Product Variable

The FREQ Procedure

Cumulative CumulativeProduct Frequency Percent Frequency Percent-------------------------------------------------------------------Boot 52 13.16 52 13.16Men's Casual 45 11.39 97 24.56Men's Dress 50 12.66 147 37.22Sandal 49 12.41 196 49.62Slipper 52 13.16 248 62.78Sport Shoe 51 12.91 299 75.70Women's Casual 45 11.39 344 87.09Women's Dress 51 12.91 395 100.00

Sashelp.snacks — Daily Snack Food Sales F 143

Sashelp.snacks — Daily Snack Food SalesThe Sashelp.snacks data set provides daily snack food sales. The following steps display information aboutthe data set Sashelp.snacks and create Figure 1.84. The data set contains 35,770 observations.

title "Sashelp.snacks --- Daily Snack Food Sales";proc contents data=sashelp.snacks varnum;


title "The First Five Observations Out of 35770";proc print data=sashelp.snacks(obs=5) heading=h noobs;run;

title "The Product Variable";proc freq data=sashelp.snacks;

tables Product;run;

Figure 1.84 Sashelp.snacks — Daily Snack Food Sales

Sashelp.snacks --- Daily Snack Food Sales




1 QtySold Num 8 Quantity sold2 Price Num 8 Retail price of product3 Advertised Num 8 Advertised (1=yes)4 Holiday Num 8 Holiday (1=yes)5 Date Num 8 DATE9. Date of sale6 Product Char 40 Product name


QtySold Price Advertised Holiday Date Product

0 1.99 0 0 01JAN2002 Baked potato chips0 1.99 0 0 02JAN2002 Baked potato chips0 1.99 0 0 03JAN2002 Baked potato chips0 1.99 0 0 04JAN2002 Baked potato chips0 1.99 0 0 05JAN2002 Baked potato chips



The Product Variable

The FREQ Procedure

Product name

Cumulative CumulativeProduct Frequency Percent Frequency Percent------------------------------------------------------------------------------Baked potato chips 1022 2.86 1022 2.86Barbeque pork rinds 1022 2.86 2044 5.71Barbeque potato chips 1022 2.86 3066 8.57Bread sticks 1022 2.86 4088 11.43Buttery popcorn 1022 2.86 5110 14.29Carmelized popcorn 1022 2.86 6132 17.14Cheddar cheese break sticks 1022 2.86 7154 20.00Cheddar cheese popcorn 1022 2.86 8176 22.86Cheese puffs 1022 2.86 9198 25.71Classic potato chips 1022 2.86 10220 28.57Easy dip tortilla chips 1022 2.86 11242 31.43Extra hot pork rinds 1022 2.86 12264 34.29Fiesta sticks 1022 2.86 13286 37.14Fried pork rinds 1022 2.86 14308 40.00Hot spicy cheese puffs 1022 2.86 15330 42.86Jalepeno sticks 1022 2.86 16352 45.71Jumbo pretzel sticks 1022 2.86 17374 48.57Low-fat popcorn 1022 2.86 18396 51.43Low-fat saltines 1022 2.86 19418 54.29Multigrain chips 1022 2.86 20440 57.14Pepper sticks 1022 2.86 21462 60.00Pretzel sticks 1022 2.86 22484 62.86Pretzel twists 1022 2.86 23506 65.71Ruffled potato chips 1022 2.86 24528 68.57Rye crackers 1022 2.86 25550 71.43Salt and vinegar potato chips 1022 2.86 26572 74.29Saltine crackers 1022 2.86 27594 77.14Shredded wheat crackers 1022 2.86 28616 80.00Stone-ground wheat sticks 1022 2.86 29638 82.86Sun-dried tomato multigrain chips 1022 2.86 30660 85.71Tortilla chips 1022 2.86 31682 88.57WOW cheese puffs 1022 2.86 32704 91.43WOW potato chips 1022 2.86 33726 94.29WOW tortilla chips 1022 2.86 34748 97.14Wheat crackers 1022 2.86 35770 100.00

Sashelp.span_multi F 145

Sashelp.span_multiThe following steps display information about the data set Sashelp.span_multi and create Figure 1.85. Thedata set contains 576 observations.

title "Sashelp.span_multi";proc contents data=sashelp.span_multi varnum;


title "The First Five Observations Out of 576";proc print data=sashelp.span_multi(obs=5) heading=h noobs;run;

Figure 1.85 Sashelp.span_multi

Sashelp.span_multi






Term

a caboademás deantes decon el fin decuadragésimo quinto

Role

AdjAdjAdjAdjAdj


Sashelp.steel — Iron/Steel Exports (Yearly: 1937-1980)The Sashelp.steel data set provides iron/steel exports (yearly: 1937–1980). The following steps displayinformation about the data set Sashelp.steel and create Figure 1.86. The data set contains 44 observations.

title "Sashelp.steel --- Iron/Steel Exports (Yearly: 1937-1980)";proc contents data=sashelp.steel varnum;


title "The First Five Observations Out of 44";proc print data=sashelp.steel(obs=5) heading=h noobs;run;

Figure 1.86 Sashelp.steel — Iron/Steel Exports (Yearly: 1937-1980)

Sashelp.steel --- Iron/Steel Exports (Yearly: 1937-1980)




1 DATE Num 8 YEAR.2 STEEL Num 8 iron/steel exports in million tons


DATE STEEL

1937 3.891938 2.411939 2.801940 8.721941 7.12

Sashelp.stocks — Performance of Three Stocks from 1996 to 2005 F 147

Sashelp.stocks — Performance of Three Stocks from 1996 to2005The Sashelp.stocks data set provides the performance of three stocks from 1996 to 2005. The followingsteps display information about the data set Sashelp.stocks and create Figure 1.87. The data set contains 699observations.

title "Sashelp.stocks --- Performance of Three Stocks from 1996 to 2005";proc contents data=sashelp.stocks varnum;


title "The First Five Observations Out of 699";proc print data=sashelp.stocks(obs=5) heading=h noobs;run;

Figure 1.87 Sashelp.stocks — Performance of Three Stocks from 1996 to 2005

Sashelp.stocks --- Performance of Three Stocks from 1996 to 2005




1 Stock Char 92 Date Num 8 DATE. DATE.3 Open Num 8 DOLLAR8.2 BEST32.4 High Num 8 DOLLAR8.2 BEST32.5 Low Num 8 DOLLAR8.2 BEST32.6 Close Num 8 DOLLAR8.2 BEST32.7 Volume Num 8 COMMA12. BEST32.8 AdjClose Num 8 DOLLAR8.2 BEST32. Adjusted Close


Stock Date Open High Low Close Volume AdjClose

IBM 01DEC05 $89.15 $89.92 $81.56 $82.20 5,976,252 $81.37IBM 01NOV05 $81.85 $89.94 $80.64 $88.90 5,556,471 $88.01IBM 03OCT05 $80.22 $84.60 $78.70 $81.88 7,019,666 $80.86IBM 01SEP05 $80.16 $82.11 $76.93 $80.22 5,772,280 $79.22IBM 01AUG05 $83.00 $84.20 $79.87 $80.62 4,801,386 $79.62


Sashelp.syr1001 — M-Competition 1001 Series, SemiannualThe Sashelp.syr1001 data set provides m-competition 1001 series, semiannual. The following steps displayinformation about the data set Sashelp.syr1001 and create Figure 1.88. The data set contains 105 observations.

title "Sashelp.syr1001 --- M-Competition 1001 Series, Semiannual";proc contents data=sashelp.syr1001 varnum;


title "The First Five Observations Out of 105";proc print data=sashelp.syr1001(obs=5) heading=h noobs;run;

Figure 1.88 Sashelp.syr1001 — M-Competition 1001 Series, Semiannual

Sashelp.syr1001 --- M-Competition 1001 Series, Semiannual



# Variable Type Len

1 T Num 42 S0502 Num 53 S0666 Num 5


T S0502 S0666

1 425 1.800002 425 1.520003 421 1.510004 322 1.800005 414 1.85000

Sashelp.tgrmapc — US County Names and State/County FIPS Codes F 149

Sashelp.tgrmapc — US County Names and State/County FIPSCodesThe Sashelp.tgrmapc data set provides US county names and state/county FIPS codes. The following stepsdisplay information about the data set Sashelp.tgrmapc and create Figure 1.89. The data set contains 3,143observations.

title "Sashelp.tgrmapc --- US County Names and State/County FIPS Codes";proc contents data=sashelp.tgrmapc varnum;


title "The First Five Observations Out of 3143";proc print data=sashelp.tgrmapc(obs=5) heading=h noobs;run;

Figure 1.89 Sashelp.tgrmapc — US County Names and State/County FIPS Codes

Sashelp.tgrmapc --- US County Names and State/County FIPS Codes




1 STATE Num 7 State FIPS Code2 COUNTY Num 7 County FIPS Code3 COUNTYNM Char 25 County Name


STATE COUNTY COUNTYNM

1 1 Autauga1 3 Baldwin1 5 Barbour1 7 Bibb1 9 Blount


Sashelp.tgrmaps — US State Names and FIPS CodesThe Sashelp.tgrmaps data set provides US state names and FIPS codes. The following steps displayinformation about the data set Sashelp.tgrmaps and create Figure 1.90. The data set contains 51 observations.

title "Sashelp.tgrmaps --- US State Names and FIPS Codes";proc contents data=sashelp.tgrmaps varnum;


title "The First Five Observations Out of 51";proc print data=sashelp.tgrmaps(obs=5) heading=h noobs;run;

Figure 1.90 Sashelp.tgrmaps — US State Names and FIPS Codes

Sashelp.tgrmaps --- US State Names and FIPS Codes




1 STATE Num 7 State FIPS Code2 STATEN Char 20


STATE STATEN

1 Alabama2 Alaska4 Arizona5 Arkansas6 California

Sashelp.thick — Coal Seam Thickness F 151

Sashelp.thick — Coal Seam ThicknessThe Sashelp.Thick data set simulates measurements of coal seam thickness (in feet) taken over an approxi-mately square area. The variable Thick contains the thickness values. The coordinates are offsets from a pointin the southwest corner of the measurement area, where the unit for the north and east distances is 1,000 feet.The following steps display information about the data set Sashelp.thick and create Figure 1.91. The data setcontains 75 observations.

title "Sashelp.thick --- Coal Seam Thickness";proc contents data=sashelp.thick varnum;


title "The First Five Observations Out of 75";proc print data=sashelp.thick(obs=5) heading=h noobs;run;

Figure 1.91 Sashelp.thick — Coal Seam Thickness

Sashelp.thick --- Coal Seam Thickness




1 East Num 82 North Num 83 Thick Num 8 Coal Seam Thickness


East North Thick

0.7 59.6 34.12.1 82.7 42.24.7 75.1 39.54.8 52.8 34.35.9 67.1 37.0


Sashelp.timedata — Time-Stamped DataThe Sashelp.timedata data set provides time-stamped data. The following steps display information aboutthe data set Sashelp.timedata and create Figure 1.92. The data set contains 40,330 observations.

title "Sashelp.timedata --- Time-Stamped Data";proc contents data=sashelp.timedata varnum;


title "The First Five Observations Out of 40330";proc print data=sashelp.timedata(obs=5) heading=h noobs;run;

Figure 1.92 Sashelp.timedata — Time-Stamped Data

Sashelp.timedata --- Time-Stamped Data




1 volume Num 8 Volume2 datetime Num 8 DATETIME18. Datetime of recordings


volume datetime

1 25JUL97:00:00:004 25JUL97:00:30:002 25JUL97:01:00:001 25JUL97:02:00:002 25JUL97:02:30:00

Sashelp.tourism — Tourism Demand Modelling and Forecasting F 153

Sashelp.tourism — Tourism Demand Modelling andForecastingThe Sashelp.tourism data set provides tourism demand modeling and forecasting. The following steps displayinformation about the data set Sashelp.tourism and create Figure 1.93. The data set contains 29 observations.

title "Sashelp.tourism --- Tourism Demand Modelling and Forecasting";proc contents data=sashelp.tourism varnum;


title "The First Five Observations Out of 29";proc print data=sashelp.tourism(obs=5) heading=h noobs;run;

Figure 1.93 Sashelp.tourism — Tourism Demand Modelling and Forecasting

Sashelp.tourism --- Tourism Demand Modelling and Forecasting




1 year Num 8 YEAR4. Year2 vsp Num 8 the number of holidays in Spain taken by US residents3 pdi Num 8 UK real personal disposable income4 puk Num 8 the implicit deflator of UK consumer expenditure5 exuk Num 8 an exchange rate index of the

UK pound against the US dollar6 pop Num 8 the UK population7 cpisp Num 8 the consumer price index in Spain8 exsp Num 8 an exchange rate index of Spanish

pesetas against the US dollar


year vsp pdi puk exuk pop cpisp exsp

1966 1.2823 201207 0.13425 0.48571 54.643 0.09616 0.435191967 1.2718 204171 0.13774 0.56320 54.959 0.10231 0.505551968 1.5370 207772 0.14430 0.56836 55.216 0.10743 0.506421969 1.9501 209684 0.15227 0.56452 55.461 0.10982 0.508161970 1.8300 217675 0.16125 0.56616 55.632 0.11358 0.50569


Sashelp.usecon — Source: US BEA, "Business Statistics"The following steps display information about the data set Sashelp.usecon and create Figure 1.94. The dataset contains 252 observations.

title "Sashelp.usecon --- Source: US BEA, ""Business Statistics""";proc contents data=sashelp.usecon varnum;


title "The First Five Observations Out of 252";proc print data=sashelp.usecon(obs=5) heading=h noobs;run;

Figure 1.94 Sashelp.usecon — Source: US BEA, "Business Statistics"

Sashelp.usecon --- Source: US BEA, "Business Statistics"




1 DATE Num 5 MONYY.2 AIRRPMD Num 5 Airline Revenue Passenger Miles Domestic3 AIRRPMT Num 5 Airline Revenue Passenger Miles Total4 CHEMICAL Num 5 Sales: Chemicals and Allied Products5 COAL Num 5 Bituminous Coal Production6 DURABLES Num 5 Sales: Durable Goods Industries, Total7 HS1FAM Num 5 Housing Starts, One-family structures8 HSTOTAL Num 5 Housing Starts, Total Private9 NONDUR Num 5 Sales: Nondurable Goods Industries Total

10 PETROL Num 5 Sales: Petroleum and Coal Products11 TOBACCO Num 5 Sales: Tobacco Products12 VEHICLES Num 5 Sales: Motor Vehicles and Parts


DATE AIRRPMD AIRRPMT CHEMICAL COAL DURABLES HS1FAM HSTOTAL NONDUR PETROL TOBACCO VEHICLES

JAN71 8.43999 10.5200 3896 49780 26617 . . 23314 2154 425 4367FEB71 7.20000 8.9900 4346 47029 29829 . . 25407 2250 433 5147MAR71 8.17000 10.1400 4318 56920 31336 . . 25832 2165 445 5418APR71 9.02000 11.1500 4536 54336 30484 . . 25773 2223 440 4897MAY71 8.39999 10.8400 4454 50442 31008 . . 25560 2190 458 5002

Sashelp.us_data — Apportionment, Population Change, Population Density F 155

Sashelp.us_data — Apportionment, Population Change,Population DensityThe Sashelp.us_data data set provides apportionment, population change, and population density. Thefollowing steps display information about the data set Sashelp.us_data and create Figure 1.95. The data setcontains 52 observations.

title "Sashelp.us_data --- Apportionment, Population Change, Population Density";proc contents data=sashelp.us_data varnum;


title "The First Five Observations Out of 52";proc print data=sashelp.us_data(obs=5) heading=h noobs;run;


Figure 1.95 Sashelp.us_data — Apportionment, Population Change, Population Density

Sashelp.us_data --- Apportionment, Population Change, Population Density




1 STATENAME Char 20 Name of State or Region2 DENSITY_1910 Num 8 COMMA8.1 1910_People per Square Mile3 DENSITY_1920 Num 8 COMMA8.1 1920_People per Square Mile4 DENSITY_1930 Num 8 COMMA8.1 1930_People per Square Mile5 DENSITY_1940 Num 8 COMMA8.1 1940_People per Square Mile6 DENSITY_1950 Num 8 COMMA8.1 1950_People per Square Mile7 DENSITY_1960 Num 8 COMMA8.1 1960_People per Square Mile8 DENSITY_1970 Num 8 COMMA8.1 1970_People per Square Mile9 DENSITY_1980 Num 8 COMMA8.1 1980_People per Square Mile

10 DENSITY_1990 Num 8 COMMA8.1 1990_People per Square Mile11 DENSITY_2000 Num 8 COMMA8.1 2000_People per Square Mile12 DENSITY_2010 Num 8 COMMA8.1 2010_People per Square Mile13 RANK_1910 Num 8 1910_Density Rank: Most Dense(1) to Least Dense(52)14 RANK_1920 Num 8 1920_Density Rank: Most Dense(1) to Least Dense(52)15 RANK_1930 Num 8 1930_Density Rank: Most Dense(1) to Least Dense(52)16 RANK_1940 Num 8 1940_Density Rank: Most Dense(1) to Least Dense(52)17 RANK_1950 Num 8 1950_Density Rank: Most Dense(1) to Least Dense(52)18 RANK_1960 Num 8 1960_Density Rank: Most Dense(1) to Least Dense(52)19 RANK_1970 Num 8 1970_Density Rank: Most Dense(1) to Least Dense(52)20 RANK_1980 Num 8 1980_Density Rank: Most Dense(1) to Least Dense(52)21 RANK_1990 Num 8 1990_Density Rank: Most Dense(1) to Least Dense(52)22 RANK_2000 Num 8 2000_Density Rank: Most Dense(1) to Least Dense(52)23 RANK_2010 Num 8 2010_Density Rank: Most Dense(1) to Least Dense(52)24 REPS_1910 Num 8 1910_Number of Representatives25 REPS_1920 Num 8 1920_Number of Representatives26 REPS_1930 Num 8 1930_Number of Representatives27 REPS_1940 Num 8 1940_Number of Representatives28 REPS_1950 Num 8 1950_Number of Representatives29 REPS_1960 Num 8 1960_Number of Representatives30 REPS_1970 Num 8 1970_Number of Representatives31 REPS_1980 Num 8 1980_Number of Representatives32 REPS_1990 Num 8 1990_Number of Representatives33 REPS_2000 Num 8 2000_Number of Representatives34 REPS_2010 Num 8 2010_Number of Representatives35 PEOPLE_PER_REP_1910 Num 8 COMMA10. 1910_People per Representative36 PEOPLE_PER_REP_1920 Num 8 COMMA10. 1920_People per Representative37 PEOPLE_PER_REP_1930 Num 8 COMMA10. 1930_People per Representative38 PEOPLE_PER_REP_1940 Num 8 COMMA10. 1940_People per Representative39 PEOPLE_PER_REP_1950 Num 8 COMMA10. 1950_People per Representative40 PEOPLE_PER_REP_1960 Num 8 COMMA10. 1960_People per Representative41 PEOPLE_PER_REP_1970 Num 8 COMMA10. 1970_People per Representative42 PEOPLE_PER_REP_1980 Num 8 COMMA10. 1980_People per Representative43 PEOPLE_PER_REP_1990 Num 8 COMMA10. 1990_People per Representative44 PEOPLE_PER_REP_2000 Num 8 COMMA10. 2000_People per Representative45 PEOPLE_PER_REP_2010 Num 8 COMMA10. 2010_People per Representative46 SEAT_CHANGE_1910 Num 8 1910_Seat Change47 SEAT_CHANGE_1920 Num 8 1920_Seat Change48 SEAT_CHANGE_1930 Num 8 1930_Seat Change49 SEAT_CHANGE_1940 Num 8 1940_Seat Change50 SEAT_CHANGE_1950 Num 8 1950_Seat Change51 SEAT_CHANGE_1960 Num 8 1960_Seat Change52 SEAT_CHANGE_1970 Num 8 1970_Seat Change



Sashelp.us_data --- Apportionment, Population Change, Population Density




53 SEAT_CHANGE_1980 Num 8 1980_Seat Change54 SEAT_CHANGE_1990 Num 8 1990_Seat Change55 SEAT_CHANGE_2000 Num 8 2000_Seat Change56 SEAT_CHANGE_2010 Num 8 2010_Seat Change57 POPULATION_1910 Num 8 COMMA15. 1910_Population58 POPULATION_1920 Num 8 COMMA15. 1920_Population59 POPULATION_1930 Num 8 COMMA15. 1930_Population60 POPULATION_1940 Num 8 COMMA15. 1940_Population61 POPULATION_1950 Num 8 COMMA15. 1950_Population62 POPULATION_1960 Num 8 COMMA15. 1960_Population63 POPULATION_1970 Num 8 COMMA15. 1970_Population64 POPULATION_1980 Num 8 COMMA15. 1980_Population65 POPULATION_1990 Num 8 COMMA15. 1990_Population66 POPULATION_2000 Num 8 COMMA15. 2000_Population67 POPULATION_2010 Num 8 COMMA15. 2010_Population68 CHANGE_1910 Num 8 1910_Population change expressed as %69 CHANGE_1920 Num 8 1920_Population change expressed as %70 CHANGE_1930 Num 8 1930_Population change expressed as %71 CHANGE_1940 Num 8 1940_Population change expressed as %72 CHANGE_1950 Num 8 1950_Population change expressed as %73 CHANGE_1960 Num 8 1960_Population change expressed as %74 CHANGE_1970 Num 8 1970_Population change expressed as %75 CHANGE_1980 Num 8 1980_Population change expressed as %76 CHANGE_1990 Num 8 1990_Population change expressed as %77 CHANGE_2000 Num 8 2000_Population change expressed as %78 CHANGE_2010 Num 8 2010_Population change expressed as %79 STATE Num 6 State Fips Code80 STATECODE Char 2 Two-letter Abbrev. for State Name81 DIVISION Char 18 US Divisions82 REGION Char 9 US Regions




DENSITY_ DENSITY_ DENSITY_ DENSITY_ DENSITY_ DENSITY_ DENSITY_ DENSITY_ DENSITY_STATENAME 1910 1920 1930 1940 1950 1960 1970 1980 1990

Alabama 42.2 46.4 52.3 55.9 60.5 64.5 6.8 76.9 79.8Alaska 0.1 0.1 0.1 0.1 0.2 0.4 0.5 0.7 0.1Arizona 1.8 2.9 3.8 4.4 6.6 11.5 15.6 23.9 32.3Arkansas 30.3 33.7 35.6 37.5 36.7 34.3 3.7 43.9 45.2California 15.3 2.2 36.4 44.3 6.8 100.9 128.1 151.9 19.1

DENSITY_ DENSITY_ RANK_ RANK_ RANK_ RANK_ RANK_ RANK_ RANK_ RANK_ RANK_ RANK_ RANK_2000 2010 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

87.8 94.4 25 25 24 23 24 28 28 28 27 28 291.1 1.2 52 52 52 52 52 52 52 52 52 52 52

45.2 56.3 49 49 47 47 47 43 43 42 39 38 3551.4 5.6 30 31 32 32 34 36 37 37 37 36 36217.4 239.1 38 35 31 30 22 15 15 16 14 14 13

PEOPLE_ PEOPLE_REPS_ REPS_ REPS_ REPS_ REPS_ REPS_ REPS_ REPS_ REPS_ REPS_ REPS_ PER_REP_ PER_REP_1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 1910 1920

10 10 9 9 9 8 7 7 7 7 7 213,809 0. . . . . 1 1 1 1 1 1 . .. . 1 2 2 3 4 5 6 8 9 . .7 7 7 7 6 4 4 4 4 4 4 224,921 0

11 11 20 23 30 38 43 45 52 53 53 216,051 0

PEOPLE_ PEOPLE_ PEOPLE_ PEOPLE_ PEOPLE_ PEOPLE_ PEOPLE_ PEOPLE_PER_REP_ PER_REP_ PER_REP_ PER_REP_ PER_REP_ PER_REP_ PER_REP_ PER_REP_

1930 1940 1950 1960 1970 1980 1990 2000

294,027 314,773 340,194 408,343 496,555 555,723 580,373 637,304. . . 226,167 304,067 400,481 551,947 628,933

389,375 249,631 374,794 434,054 446,905 543,573 612,998 642,585264,921 278,484 318,252 446,568 485,576 571,378 590,560 669,933283,412 300,321 352,874 413,611 467,415 525,968 573,832 640,204

PEOPLE_ SEAT_ SEAT_ SEAT_ SEAT_ SEAT_ SEAT_ SEAT_ SEAT_ SEAT_ SEAT_PER_REP_ CHANGE_ CHANGE_ CHANGE_ CHANGE_ CHANGE_ CHANGE_ CHANGE_ CHANGE_ CHANGE_ CHANGE_

2010 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

686,140 1 0 -1 0 0 -1 -1 0 0 0721,523 . . . . . 1 0 0 0 0712,522 . . 1 1 0 1 1 1 1 2731,557 0 0 0 0 -1 -2 0 0 0 0704,566 3 0 9 3 7 8 5 2 7 1




SEAT_CHANGE_2010 POPULATION_1910 POPULATION_1920 POPULATION_1930 POPULATION_1940 POPULATION_1950

0 2,138,093 2,348,174 2,646,248 2,832,961 3,061,7430 64,356 55,036 59,278 72,524 128,6431 204,354 334,162 435,573 499,261 749,5870 1,574,449 1,752,204 1,854,482 1,949,387 1,909,5110 2,377,549 3,426,861 5,677,251 6,907,387 10,586,223

POPULATION_1960 POPULATION_1970 POPULATION_1980 POPULATION_1990 POPULATION_2000 POPULATION_2010

3,266,740 3,444,165 3,893,888 4,040,587 4,447,100 4,779,736226,167 300,382 401,851 550,043 626,932 710,231

1,302,161 1,770,900 2,718,215 3,665,228 5,130,632 6,392,0171,786,272 1,923,295 2,286,435 2,350,725 2,673,400 2,915,918

15,717,204 19,953,134 23,667,902 29,760,021 33,871,648 37,253,956

CHANGE_ CHANGE_ CHANGE_ CHANGE_ CHANGE_ CHANGE_ CHANGE_ CHANGE_ CHANGE_ CHANGE_ CHANGE_1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

16.9 9.8 12.7 7.1 8.1 6.7 5.4 13.1 3.8 10.1 7.51.2 -14.5 7.7 22.3 77.4 75.8 32.8 33.8 36.9 14.0 13.3

66.2 63.5 30.3 14.6 50.1 73.7 36.0 53.5 34.8 40.0 24.620.0 11.3 5.8 5.1 -2.0 -6.5 7.7 18.9 2.8 13.7 9.160.1 44.1 65.7 21.7 53.3 48.5 27.0 18.6 25.7 13.8 10.0

STATE STATECODE DIVISION REGION

1 AL East South Central South2 AK Pacific West4 AZ Mountain West5 AR West South Central South6 CA Pacific West


Sashelp.vbplayrsThe following steps display information about the data set Sashelp.vbplayrs and create Figure 1.96. The dataset contains 11 observations.

title "Sashelp.vbplayrs";proc contents data=sashelp.vbplayrs varnum;


title "The First Five Observations Out of 11";proc print data=sashelp.vbplayrs(obs=5) heading=h noobs;run;

title "The TUESDAY and FRIDAY Variables";proc freq data=sashelp.vbplayrs;

tables TUESDAY;tables FRIDAY;

run;

Figure 1.96 Sashelp.vbplayrs

Sashelp.vbplayrs



# Variable Type Len

1 NAME Char 182 EMAIL Char 503 TUESDAY Char 14 FRIDAY Char 1


NAME EMAIL TUESDAY FRIDAY

John Doe [email protected] N YJoe Smith [email protected] N YBecky Smith [email protected] Y YCharlie Thomas [email protected] Y NDaniel Jones [email protected] N Y

Sashelp.vbplayrs F 161


The TUESDAY and FRIDAY Variables

The FREQ Procedure

Cumulative CumulativeTUESDAY Frequency Percent Frequency Percent------------------------------------------------------------N 6 54.55 6 54.55Y 5 45.45 11 100.00

Cumulative CumulativeFRIDAY Frequency Percent Frequency Percent-----------------------------------------------------------N 4 36.36 4 36.36Y 7 63.64 11 100.00


Sashelp.workers — Employment (Monthly: Jan77-Jul82)The Sashelp.workers data set provides employment data (monthly: Jan77–Jul82). The following stepsdisplay information about the data set Sashelp.workers and create Figure 1.97. The data set contains 67observations.

title "Sashelp.workers --- Employment (Monthly: Jan77-Jul82)";proc contents data=sashelp.workers varnum;


title "The First Five Observations Out of 67";proc print data=sashelp.workers(obs=5) heading=h noobs;run;

Figure 1.97 Sashelp.workers — Employment (Monthly: Jan77-Jul82)

Sashelp.workers --- Employment (Monthly: Jan77-Jul82)




1 DATE Num 8 MONYY5.2 ELECTRIC Num 8 electrical workers, thousands3 MASONRY Num 8 masonry workers, thousands


DATE ELECTRIC MASONRY

JAN77 241.9 196.6FEB77 239.3 216.3MAR77 245.5 235.1APR77 249.6 252.2MAY77 255.7 261.5

Sashelp.yr1001 — M-Competition 1001 Series, Annual F 163

Sashelp.yr1001 — M-Competition 1001 Series, AnnualThe Sashelp.yr1001 data set provides m-competition 1001 series, annual. The following steps displayinformation about the data set Sashelp.yr1001 and create Figure 1.98. The data set contains 126 observations.

title "Sashelp.yr1001 --- M-Competition 1001 Series, Annual";proc contents data=sashelp.yr1001 varnum;


title "The First Five Observations Out of 126";proc print data=sashelp.yr1001(obs=5) heading=h noobs;run;


Figure 1.98 Sashelp.yr1001 — M-Competition 1001 Series, Annual

Sashelp.yr1001 --- M-Competition 1001 Series, Annual



# Variable Type Len

1 T Num 42 S0001 Num 53 S0002 Num 54 S0003 Num 55 S0004 Num 56 S0005 Num 57 S0006 Num 58 S0007 Num 59 S0008 Num 510 S0009 Num 511 S0010 Num 512 S0011 Num 513 S0012 Num 514 S0013 Num 515 S0014 Num 516 S0015 Num 517 S0016 Num 518 S0017 Num 519 S0018 Num 520 S0019 Num 521 S0020 Num 522 S0021 Num 523 S0022 Num 524 S0023 Num 525 S0024 Num 526 S0025 Num 527 S0026 Num 528 S0027 Num 529 S0028 Num 530 S0029 Num 531 S0030 Num 532 S0031 Num 533 S0032 Num 534 S0033 Num 535 S0034 Num 536 S0035 Num 537 S0036 Num 538 S0037 Num 539 S0038 Num 540 S0039 Num 541 S0040 Num 542 S0041 Num 543 S0042 Num 544 S0043 Num 545 S0044 Num 546 S0045 Num 547 S0046 Num 548 S0047 Num 549 S0048 Num 550 S0049 Num 551 S0050 Num 552 S0051 Num 5






# Variable Type Len

53 S0052 Num 554 S0053 Num 555 S0054 Num 556 S0055 Num 557 S0056 Num 558 S0057 Num 559 S0058 Num 560 S0059 Num 561 S0060 Num 562 S0061 Num 563 S0062 Num 564 S0063 Num 565 S0064 Num 566 S0065 Num 567 S0066 Num 568 S0067 Num 569 S0068 Num 570 S0069 Num 571 S0070 Num 572 S0071 Num 573 S0072 Num 574 S0073 Num 575 S0074 Num 576 S0075 Num 577 S0076 Num 578 S0077 Num 579 S0078 Num 580 S0079 Num 581 S0080 Num 582 S0081 Num 583 S0082 Num 584 S0083 Num 585 S0084 Num 586 S0085 Num 587 S0086 Num 588 S0087 Num 589 S0088 Num 590 S0089 Num 591 S0090 Num 592 S0091 Num 593 S0092 Num 594 S0093 Num 595 S0094 Num 596 S0095 Num 597 S0096 Num 598 S0097 Num 599 S0098 Num 5

100 S0099 Num 5101 S0100 Num 5102 S0101 Num 5103 S0102 Num 5104 S0103 Num 5






# Variable Type Len







# Variable Type Len

469 S0935 Num 5470 S0941 Num 5471 S0942 Num 5472 S0943 Num 5473 S0944 Num 5474 S0945 Num 5475 S0946 Num 5476 S0947 Num 5477 S0948 Num 5478 S0949 Num 5479 S0950 Num 5480 S0951 Num 5481 S0959 Num 5482 S0960 Num 5483 S0961 Num 5484 S0962 Num 5485 S0963 Num 5486 S0964 Num 5487 S0965 Num 5488 S0966 Num 5489 S0967 Num 5490 S0968 Num 5491 S0969 Num 5492 S0970 Num 5493 S0971 Num 5494 S0972 Num 5495 S0973 Num 5496 S0974 Num 5497 S0975 Num 5498 S0976 Num 5499 S0977 Num 5500 S0979 Num 5501 S0987 Num 5502 S0988 Num 5503 S0989 Num 5504 S0996 Num 5505 S1000 Num 5




T S0001 S0002 S0003 S0004 S0005 S0006 S0007 S0008 S0009 S0010 S0011 S0012 S0013 S0014

1 3600 12654 2142 5774 432312 894.50 332381 283219 506190 19.1300 2075 2197 263 231802 7700 22879 12935 7650 569011 1501.70 352429 326815 534047 21.6700 2359 2394 992 218273 12300 34164 19130 9271 862673 1971.20 358621 408814 581982 21.1700 2101 7308 437 325674 30500 49524 30500 21447 1155640 2249.40 424889 440834 576326 23.3700 2371 22583 35 97175 47390 64761 48177 28998 1439290 3029.50 509281 460495 604878 25.3600 2700 53193 55 27319

S0015 S0016 S0017 S0018 S0019 S0020 S0021 S0022 S0023 S0024 S0025 S0026

574.900 4.97358 1045 5 2189.29 141 51.4000 2.56817 131.50 11.500 0.100 18820896519.300 5.08749 12031 8536 2830.64 863 49.3000 2.58339 576.50 143.200 65.300 18322288545.500 5.21202 20306 18971 2256.02 2245 50.0000 2.60319 1033.80 178.300 233.900 19123200562.900 5.37025 25513 32850 2698.04 5202 57.0000 2.62582 1223.20 122.100 297.200 13393000630.200 5.58649 35766 70608 3372.33 8637 61.6000 2.63720 1202.90 110.800 307.300 14257400

S0027 S0028 S0029 S0030 S0031 S0032 S0033 S0034 S0035 S0036 S0037 S0038 S0039 S0040

13553800 2022 589 553 77.200 111 1565 74.400 9160 186.250 54.2000 3315 3007 618013198800 9193 635 631 130.500 115 1805 121.000 8837 163.980 65.1000 3924 3010 655513535696 3687 671 546 136.200 146 1695 116.900 7907 168.010 97.0000 3838 3192 65339686224 3554 671 601 108.900 139 1969 97.000 7104 192.570 91.5000 3874 3323 6692

10267800 3046 671 699 109.900 131 2301 95.400 7103 188.930 86.7000 4082 3680 6940


15858 40 248 7400 1083 3022 35 5 12031 4388 119 402240 137598 4351 992316878 40 261 7413 2666 4177 188 8536 20306 4851 111 416673 151945 10303 1138716168 40 261 7254 2393 3751 318 18971 25513 5322 163 429306 153433 14335 1542316782 52 290 6226 3109 5320 425 32580 35766 5018 203 422028 187552 23996 2940217006 80 315 6534 5071 8397 493 70608 37868 5037 197 440946 187981 57704 25348

S0056 S0057 S0058 S0059 S0060 S0061 S0062 S0063 S0064 S0065 S0066 S0067 S0068

83702 23350 67.500 11460 109.000 1560 607 242000 3821700 2440.40 68 149020 120.00078925 23685 62.300 23150 94.400 506 377 281243 2875600 4046.40 81 154580 230.00087571 24427 112.400 31539 210.000 797 317 310000 3521640 4526.10 105 155500 221.30088408 24716 175.500 26390 27.800 451 155 316070 3788670 5800.30 126 154470 286.30086866 25632 238.800 17757 51.300 539 152 348174 3723270 7468.70 145 160120 429.700

S0069 S0070 S0071 S0072 S0073 S0074 S0075 S0076 S0077 S0078 S0079 S0080

7.300 22.0000 25.9000 19.4000 8.5000 246.800 142.240 27.6000 16.2450 248 897.21 19.70006.300 27.7000 35.7000 35.5000 22.8000 299.600 137.800 29.4000 16.9020 261 1410.83 21.1000

26.800 43.9000 48.5000 25.4000 26.9000 246.400 127.600 26.4000 14.6120 261 1306.81 26.9000114.500 64.5000 63.8000 22.4000 27.6000 289.800 137.600 24.5000 14.8700 290 1031.58 26.7000262.700 99.7000 74.8000 73.2000 31.1000 250.900 141.800 28.7000 17.0870 315 1508.36 22.3000




S0081 S0082 S0083 S0084 S0085 S0086 S0087 S0088 S0089 S0090 S0091 S0092

27562.19 13.6000 417 165 60.700 34.2000 1.76000 4.48000 11.4000 7.15000 2.14000 2.7200036027.69 21.2000 451 172 91.100 49.3000 4.13000 4.52000 11.5000 7.89000 2.07000 2.5800032254.00 18.6000 360 224 106.200 52.2000 7.04000 3.71000 11.8000 8.28999 2.53000 2.8600033906.59 21.6000 525 224 112.200 55.5000 7.82000 4.30000 10.6000 8.10000 2.40000 2.8300043264.88 21.2000 392 151 120.100 57.5000 6.62000 5.66000 11.4000 8.05000 2.54000 3.15000

S0093 S0094 S0095 S0096 S0097 S0098 S0099 S0100 S0101 S0102 S0103 S0104 S0105 S0106

137.310 35.9200 35.6400 5.97000 54 56.3700 96 67 86 33.0000 191562 34666 53841 6545147.780 38.0600 37.5600 3.89000 58 58.2400 96 60 86 34.9000 206884 55413 56919 10170152.390 44.0500 36.1600 3.87000 60 54.5500 80 85 71 37.6000 217342 69517 57136 4555158.980 45.4900 36.4000 1.68000 62 60.4300 74 96 66 43.7000 230677 73404 60905 4461164.730 41.4500 39.5400 3.73000 65 66.4700 85 118 80 44.6000 237258 68004 62833 711

S0107 S0108 S0109 S0110 S0111 S0112 S0113 S0114 S0115 S0116 S0117 S0118 S0119

13807 242167 25.2900 237 232 474.000 2.70000 4.70000 47.4000 28.9000 11.5000 6.70000 2.5000018744 279232 21.6800 273 264 486.900 3.10000 5.20000 52.6000 31.1000 11.8000 6.60000 2.7000017992 293180 18.8300 286 266 484.000 3.40000 5.30000 57.3000 32.5000 12.1000 6.70000 2.7000016934 306635 18.1700 261 254 490.500 3.80000 5.50000 61.9000 34.9000 13.1000 7.10000 3.1000017746 304918 22.8500 287 275 503.000 3.50000 5.90000 66.3000 36.3000 14.0000 7.40000 3.20000

S0120 S0121 S0122 S0123 S0124 S0125 S0126 S0127 S0128 S0129 S0130 S0131

3.70000 4.40000 2.20000 49.6400 15.5600 9.7800 5.66000 57.1400 14.4800 21.4700 14.3800 15.40004.10000 4.60000 2.30000 51.7500 17.2200 9.9200 6.79000 62.3700 14.1400 24.0900 13.8100 16.30004.20000 4.80000 2.30000 53.1600 17.4000 10.0300 7.16000 64.8700 17.5200 26.5900 12.3000 15.54004.20000 5.00000 2.40000 56.0100 18.0600 10.1900 7.60000 67.2800 20.3200 24.9600 11.9200 15.72004.60000 5.20000 2.60000 57.9700 18.5900 10.5000 8.53000 69.1400 16.1600 25.3900 12.1800 16.8000


53473 20965 21275 25585 14243 20423 19810 18654 28.9000 7929980 2631 1214 841 586 81459926 22623 23619 24510 32555 21858 17922 20651 31.0000 9001736 2898 1288 886 662 107463014 23457 26454 23821 46046 23471 17930 20216 33.1000 9794088 3185 1272 934 686 144063903 23614 29291 27703 48715 24689 18669 21503 34.9000 10002096 3500 1296 1001 832 152164947 23710 31643 27313 41519 26485 20109 20552 36.5000 10403296 3695 1391 1056 966 1515

S0147 S0148 S0149 S0150 S0151 S0152 S0153 S0154 S0155 S0156 S0157 S0158 S0159 S0160 S0161 S0162

1312 331 469 900 1552 159 30450 223 2067 498 372 369 741 897 965 28221420 376 511 1002 1418 110 30700 236 2261 501 392 385 777 903 973 28591536 494 564 1048 1394 80 30940 262 2102 496 376 367 743 894 963 46021604 630 597 1132 1450 95 31190 238 1590 508 386 378 764 913 984 37401695 645 655 1252 1561 141 31410 243 1768 503 401 399 800 904 973 3852




S0163 S0164 S0165 S0166 S0167 S0168 S0169 S0170 S0171 S0172 S0173 S0174 S0175

40.9000 40.4000 1305 24.7000 9.60000 398464 2.47000 1586570 393 24152 418 393 30719840.5000 39.8000 2219 25.0000 9.20000 386768 2.35000 1674530 414 24217 453 425 30595840.1000 39.2000 1922 24.7000 9.30000 382665 2.30000 1602380 413 24317 410 386 31032240.0000 40.3000 1029 24.9000 9.30000 393754 2.28000 1547070 428 24401 492 464 31031140.0000 39.7000 980 25.0000 9.60000 391853 2.24000 1553470 450 24510 528 498 308746

S0176 S0177 S0178 S0179 S0180 S0181 S0182 S0183 S0186 S0187 S0190 S0193 S0194 S0197

26139 290783 11693 8438 55.9100 564 0.54000 97 503674 462.914 3676 205 587 1916826214 285242 11702 8689 54.7000 841 0.78000 95 549712 430.743 1190 219 717 1627926276 293718 11703 8590 55.3000 854 0.82000 95 547329 402.723 1972 198 571 1276726364 295804 11557 8763 55.7500 689 0.46000 94 555894 389.922 2711 226 523 1311626436 294582 11951 8710 55.4600 559 0.52000 93 544511 421.822 2621 234 457 13141

S0198 S0200 S0201 S0202 S0203 S0204 S0208 S0209 S0210 S0211 S0214 S0215 S0216 S0218

393004 21194 462914 101802 113113 135.608 156.200 13.7000 42413 489 81042 839 1173 104374303 13377 430743 98612 109569 132.218 166.300 10.9000 42968 698 97471 835 1439 109417670 11982 402723 108001 120001 127.232 173.900 12.9000 25729 861 100051 839 1657 156414987 12072 389922 105627 117363 125.238 177.600 13.9000 41482 958 94505 869 1425 174436997 11927 421822 103518 115020 126.036 192.700 5.6000 60789 461 92247 840 1675 243

S0220 S0221 S0222 S0223 S0227 S0232 S0239 S0241 S0242 S0243 S0244 S0245 S0246 S0247

1259 63788 13313 49954 0.78000 99 2989.43 113113 22 65 108 15.6000 66 152.2002202 59714 10780 16602 0.83000 107 2847.18 109569 22 66 110 16.0000 71 152.5003071 68502 10744 23786 0.88000 104 3177.06 120001 21 67 106 16.0000 73 154.7002732 71273 12584 13457 1.04000 105 3156.65 117363 21 68 104 15.8000 73 157.6003836 81679 15299 10060 0.94000 113 3324.07 115020 21 67 104 15.8000 75 161.300

S0248 S0249 S0250 S0251 S0252 S0253 S0254 S0255 S0256 S0257 S0258 S0259

73.6000 121.600 28.5600 51.7700 105.300 84 103.400 102.600 103.200 104.300 1031.20 547273.3000 124.800 29.7700 61.4200 107.600 85 104.840 104.500 105.300 106.100 1052.90 588474.4000 129.200 28.3000 61.1200 103.100 87 106.730 106.200 107.200 107.500 1078.10 631676.5000 132.400 32.8500 61.7300 106.200 88 109.010 108.800 108.500 109.800 1106.80 648878.6000 138.500 31.8900 61.8900 103.600 87 111.580 111.800 110.900 113.200 1122.30 6720

S0260 S0261 S0262 S0263 S0264 S0265 S0268 S0269 S0271 S0272 S0273 S0274 S0275 S0279

6176 27244 7727 2631 8171 1473 226 19308 13.9000 13.6000 2.70000 11.1000 26.200 368.3005972 28140 7816 2648 8270 1435 204 20088 15.6000 13.5000 6.10000 14.0000 42.600 451.4006092 29140 8171 2836 8744 1509 203 20660 15.2000 13.9000 3.20000 15.9000 68.000 472.4006116 29588 8382 2835 9099 1689 212 21212 15.1000 13.8000 8.39999 17.1000 93.400 522.0006172 30936 8511 2651 9322 1850 193 21396 16.0000 13.7000 3.00000 14.9000 193.500 461.100




S0281 S0282 S0283 S0284 S0288 S0291 S0294 S0295 S0298 S0299 S0300 S0302

101.600 132.100 82.0000 6024 105.500 2830 262 49.7000 299.000 197.500 66.1000 69.700099.400 176.700 81.8000 5960 109.900 3012 258 53.8000 304.100 201.700 67.1000 71.6000

102.300 171.700 81.4000 6088 63.000 3032 270 55.1000 311.200 203.600 69.6000 73.6000103.400 215.200 81.1000 6140 80.300 3232 286 49.0000 316.400 205.700 72.0000 75.5000103.500 119.600 80.7000 6356 114.700 3242 279 39.0000 320.400 212.000 73.4000 76.0000

S0303 S0304 S0305 S0306 S0309 S0310 S0311 S0312 S0313 S0314 S0315 S0316 S0319

11.1200 474.000 94.3000 28.0000 688 2777 70.3000 48.4000 7967 20356 17332 277 51.190011.6100 486.900 95.1000 30.8000 692 2827 73.5000 48.8000 8036 21040 17688 130 56.640011.5900 484.000 94.3000 27.9000 695 2864 78.1000 49.3000 8106 21220 17768 279 56.960011.6200 490.500 94.2000 27.0000 696 2924 81.1000 49.8000 8176 21852 18200 489 60.410011.8200 503.000 93.9000 28.9000 702 2980 82.7000 50.0000 8246 22320 18388 555 60.8700

S0320 S0321 S0327 S0331 S0334 S0339 S0340 S0341 S0342 S0343 S0346 S0347 S0348

12.4600 35.5000 209.600 79.9000 2.15000 235 49.8000 510.100 48.6000 69.8000 20 54 812.4100 36.0000 211.600 84.8000 2.36000 248 52.2000 516.600 49.3000 71.1000 21 65 813.5300 35.2000 211.700 85.1000 1.57000 264 54.2000 522.000 50.4000 72.6000 23 79 713.4200 33.7000 214.800 85.3000 1.45000 285 56.6000 535.700 52.8000 75.1000 24 79 613.9900 32.5000 216.500 88.2000 2.43000 290 61.2000 544.700 53.9000 76.3000 27 71 5

S0349 S0350 S0353 S0354 S0355 S0356 S0357 S0358 S0359 S0362 S0372 S0373 S0377 S0379

50 5.13300 98.200 95.700 119 26 18 55 73 183 119 60 41.0000 6250 5.15500 99.100 97.500 113 24 19 41 71 202 328 90 41.1000 6349 5.17400 100.200 100.000 110 25 15 42 65 207 200 104 41.4000 6351 5.20100 100.700 102.200 105 26 11 43 59 210 290 101 40.9000 6354 5.22100 102.500 104.700 98 27 8 42 52 210 471 78 41.0000 64

S0385 S0386 S0392 S0399 S0400 S0402 S0408 S0411 S0424 S0425 S0430 S0431 S0432 S0433

697458 61.6094 1061 372.833 155 1830.96 885 682 180 140 175.245 2285 1396 6.781061187650 66.7435 1237 377.494 155 1356.26 870 626 147 250 176.459 2396 1548 6.822201069690 71.8776 1613 559.250 159 1581.64 866 1084 148 222 182.810 2390 2131 6.863801078430 82.1458 1506 442.740 159 1717.27 859 1133 137 210 191.282 1704 1337 6.502791059910 87.2800 1306 410.117 157 3035.64 862 896 179 200 184.891 2099 1267 6.56667

S0443 S0447 S0478 S0482 S0483 S0484 S0485 S0494 S0500 S0517 S0518 S0519 S0530 S0531

105.500 73.266 338.300 587 587 230 14.9525 36500 54 8605 3645 494 1732 386105.400 79.371 331.400 717 717 231 14.1739 34800 151 12337 5203 7037 2760 623115.400 85.477 332.200 571 571 231 16.1850 22900 346 11647 4313 8029 2092 350123.100 97.688 372.800 523 523 231 17.0200 29800 407 9730 6015 8453 2728 472129.100 103.793 369.400 457 457 231 16.0844 32450 540 9462 6127 10756 1462 443




S0532 S0535 S0549 S0550 S0551 S0553 S0555 S0556 S0558 S0561 S0564 S0568 S0582 S0583

475 8.20000 1446 2346 6287 613 217 48 508 7.20000 139220 16.4636 16923.30 431.709512 7.90000 1446 3388 7406 696 229 38 480 6.20000 146061 18.4047 14353.90 403.534610 9.60000 1664 4020 8743 402 473 47 518 6.40000 144560 17.2416 11198.30 387.630639 8.60000 1601 3476 5571 285 1144 34 415 6.70000 133360 18.4080 12787.80 434.363613 9.39999 1504 2497 6309 1215 1649 40 440 6.10000 132701 23.4722 12186.50 412.445

S0586 S0602 S0603 S0604 S0612 S0636 S0637 S0638 S0639 S0650 S0651 S0654 S0656

843.239 2.38000 450 123 5428 1500 1430 2545 757 57.6000 10.7900 6.80000 0.56000842.104 2.49000 383 124 5345 1773 1600 2620 1061 53.4000 9.9000 7.29000 0.55000841.644 2.39000 424 123 6166 1591 810 3628 1040 55.5000 10.3600 7.98000 0.79000849.009 1.77000 377 125 6015 1996 650 3682 1288 55.5000 10.4700 9.34000 0.72000828.287 1.63000 358 125 6144 2384 1345 3161 1304 59.6000 9.8800 9.39999 0.79000

S0660 S0677 S0679 S0682 S0685 S0686 S0689 S0691 S0693 S0696 S0707 S0708 S0709

9.92000 151.800 73 80.2000 28.7000 149 310 26.4700 183 1046 3.96000 9.4300 2.000007.92000 150.400 144 72.5000 28.4000 197 350 27.3700 170 1038 3.44000 12.1900 1.950007.96000 160.000 149 66.1000 27.1000 216 346 27.4000 163 1124 3.45000 9.6100 2.060009.18999 167.000 155 52.2000 31.2000 288 352 23.8600 168 1020 4.07000 12.3500 2.000007.65000 160.300 147 80.7000 31.0000 274 308 35.7400 168 819 4.19000 10.0000 1.91000

S0711 S0713 S0717 S0719 S0720 S0721 S0722 S0723 S0724 S0725 S0726 S0727 S0728

7.51000 0.47000 10.2300 248 930 7359 712 335 1677 23.8000 13.3700 15.2500 5.530006.46000 0.77000 9.1200 458 848 7274 532 411 1829 22.7000 11.1400 10.7400 4.590006.17000 0.84000 8.0000 411 983 7175 781 431 1998 21.5000 15.0000 10.6000 4.900008.50000 1.16000 10.2600 196 630 7124 560 258 1903 22.8000 14.9000 12.7400 5.900009.95000 1.02000 9.8400 267 610 7319 795 203 1880 22.2000 14.5500 14.4800 5.14000

S0729 S0730 S0764 S0772 S0773 S0774 S0775 S0776 S0777 S0778 S0779 S0781

7.53000 4.80000 35889 373.300 1987 1089 1.85600 104.600 110.800 108.100 114.600 106.5006.91000 4.93000 33259 375.900 2143 1510 1.84300 105.000 112.800 110.600 113.800 106.5007.42000 4.03000 34508 377.700 1954 1485 1.84400 104.500 112.900 111.600 113.100 108.2007.72000 3.48000 34860 378.500 1927 1412 1.84800 105.500 112.700 112.200 115.500 111.4007.69000 4.64000 34649 379.000 1899 1409 1.85800 105.900 114.600 117.200 115.100 113.900

S0782 S0788 S0797 S0804 S0805 S0806 S0807 S0808 S0809 S0810 S0812 S0813 S0814

97.2000 59 678.800 2860 77123 2860 102.900 110.900 104.100 124.100 106.400 99.700 118.50099.0000 62 678.300 2846 77347 2812 103.000 117.800 112.800 127.400 110.100 97.300 118.10098.0000 62 679.500 3116 78986 3016 102.500 117.800 112.200 128.600 108.300 96.900 117.40098.2000 62 683.500 2173 79429 2173 103.600 113.700 103.200 133.900 110.700 101.000 119.40098.4000 62 688.900 2309 79241 1903 103.900 123.100 108.300 151.400 111.700 102.600 118.500




S0815 S0816 S0817 S0818 S0819 S0820 S0821 S0823 S0824 S0827 S0828 S0829 S0830

114.000 94.2000 94.0000 90.6000 82.9000 105.500 88.6000 184.300 82 2077 5023 62301 4798112.600 96.0000 91.5000 94.3000 82.2000 107.000 89.1000 185.900 78 2087 5004 63004 4821111.800 95.0000 93.4000 92.4000 81.3000 106.600 87.2000 186.500 77 2095 5057 63616 4847112.700 95.1000 92.4000 91.2000 82.1000 107.100 87.3000 185.500 74 2109 5132 64594 4883113.200 94.4000 92.4000 91.5000 79.5000 107.600 87.3000 187.000 81 2114 5258 65455 4916

S0831 S0832 S0833 S0834 S0836 S0837 S0838 S0839 S0843 S0844 S0845 S0846 S0848

1262 112.460 43.9600 52.5600 1603 19.1600 42 43562 10.8200 101.400 106.500 104.600 126.2001285 112.990 44.0000 53.1800 1820 22.8000 50 44618 11.4900 99.100 105.900 101.900 128.2001293 114.280 44.2900 53.9700 1517 21.8800 55 45563 12.2900 102.800 113.100 100.200 128.4001304 114.890 44.6100 54.9200 1448 21.2400 53 46203 13.5400 105.000 117.100 102.700 129.5001307 115.580 45.0600 56.1100 1467 21.6100 50 47209 14.9900 107.700 120.200 101.000 131.200

S0849 S0855 S0856 S0858 S0863 S0870 S0874 S0880 S0881 S0882 S0883 S0884

105.400 106.800 210 157 187 3.22000 3.42000 101.200 101.000 102.600 105.000 102.100109.200 114.500 178 158 201 2.18000 3.86000 101.400 99.800 99.900 101.100 102.700113.100 115.100 208 159 205 2.34000 3.71000 102.400 102.100 102.300 100.600 109.500115.000 123.700 146 161 223 2.83000 3.85000 104.500 103.600 105.400 102.700 122.000117.000 112.700 162 160 222 2.81000 3.56000 105.100 103.800 106.100 105.900 122.800

S0885 S0886 S0887 S0888 S0889 S0890 S0891 S0892 S0893 S0894 S0895 S0896

107.700 102.900 101.100 101.600 104.800 104.100 104.100 106.800 102.200 103.800 103.100 105.700108.800 103.000 99.900 101.300 108.100 106.100 101.000 103.400 99.400 103.600 101.700 105.000111.600 111.600 102.000 106.800 118.400 111.200 108.600 102.700 100.300 113.300 105.000 111.500109.300 120.300 102.000 111.400 126.200 117.000 111.800 104.400 100.400 116.400 107.100 114.100112.900 123.900 101.500 118.600 130.900 126.000 116.000 110.600 104.000 123.100 115.700 116.100

S0902 S0915 S0916 S0917 S0918 S0919 S0920 S0921 S0922 S0923 S0924 S0925 S0926 S0928

176.800 4.60000 138.800 32.0000 4029 63724 153 72356 1361 3.62000 940 21046 583 134238.100 4.40000 132.600 75.9000 3932 64188 160 72683 1278 3.94000 943 21143 500 142228.100 4.10000 137.300 76.1000 3950 64397 150 72713 1443 3.65000 945 21296 708 142187.900 4.60000 138.100 77.2000 3918 64942 149 73274 1524 3.85000 945 21472 687 143151.900 4.20000 142.800 77.5000 3764 65028 155 73395 1483 3.68000 947 21762 583 143

S0929 S0930 S0935 S0941 S0942 S0943 S0944 S0945 S0946 S0947 S0948 S0949 S0950

50 119 13.3000 8.09000 1.06000 1.49000 3.15000 0.94100 0.37000 497 1126 2131 109752 120 14.5000 7.60000 1.18000 1.21000 3.24000 0.80400 0.30000 322 1182 1910 70852 120 13.4000 9.28000 1.30000 1.58000 3.43000 0.90500 0.46000 865 1491 2486 134449 122 11.9000 7.13000 0.97000 1.24000 2.59000 0.96700 0.38000 757 1184 1953 93747 122 12.0000 7.08000 0.87000 1.20000 2.59000 0.61400 0.63000 637 1139 1927 799




S0951 S0959 S0960 S0961 S0962 S0963 S0964 S0965 S0966 S0967 S0968 S0969

7906 85707 1198 5.90000 4.20000 5.50000 17.8000 5.30000 10.6000 3.00000 3.60000 7.100007114 85535 1294 5.70000 4.00000 5.00000 18.8000 5.10000 10.5000 2.80000 3.30000 7.000008270 86313 1224 5.90000 4.10000 5.40000 17.9000 5.30000 10.5000 2.80000 3.50000 6.900005896 86284 1137 5.90000 4.30000 5.40000 17.3000 5.40000 9.6000 2.90000 3.40000 6.800005813 86486 1180 5.90000 4.30000 5.90000 15.7000 5.30000 10.7000 2.90000 3.60000 6.80000

S0970 S0971 S0972 S0973 S0974 S0975 S0976 S0977 S0979 S0987 S0988 S0989 S0996 S1000

6.10000 9.8000 6.40000 6.70000 625 281 605 653 241 6 709 6899 5765 55.91005.90000 10.3000 6.00000 6.10000 626 277 598 657 247 11 765 1837 6659 56.00006.10000 9.8000 6.20000 6.30000 622 271 593 651 249 15 738 3897 869 55.45005.90000 10.6000 5.80000 5.80000 620 261 585 644 266 13 783 3420 11363 56.01006.00000 12.5000 6.00000 6.30000 620 256 582 638 280 10 1002 3123 5143 55.5400


Sashelp.yr111 — M-Competition 111 Series, AnnualThe Sashelp.yr111 data set provides m-competition 111 series, annual. The following steps display informa-tion about the data set Sashelp.yr111 and create Figure 1.99. The data set contains 126 observations.

title "Sashelp.yr111 --- M-Competition 111 Series, Annual";proc contents data=sashelp.yr111 varnum;


title "The First Five Observations Out of 126";proc print data=sashelp.yr111(obs=5) heading=h noobs;run;


Figure 1.99 Sashelp.yr111 — M-Competition 111 Series, Annual




# Variable Type Len






T S004 S013 S022 S031 S040 S049 S058 S067 S076 S085 S094 S103

1 5774 263 2.56817 77.200 6180 12031 67.500 149020 27.6000 60.700 35.9200 1915622 7650 992 2.58339 130.500 6555 20306 62.300 154580 29.4000 91.100 38.0600 2068843 9271 437 2.60319 136.200 6533 25513 112.400 155500 26.4000 106.200 44.0500 2173424 21447 35 2.62582 108.900 6692 35766 175.500 154470 24.5000 112.200 45.4900 2306775 28998 55 2.63720 109.900 6940 37868 238.800 160120 28.7000 120.100 41.4500 237258

S112 S121 S130 S139 S148 S157 S166 S175 S193 S202 S211 S220 S247

474.000 4.40000 14.3800 18654 331 372 24.7000 307198 205 101802 489 1259 152.200486.900 4.60000 13.8100 20651 376 392 25.0000 305958 219 98612 698 2202 152.500484.000 4.80000 12.3000 20216 494 376 24.7000 310322 198 108001 861 3071 154.700490.500 5.00000 11.9200 21503 630 386 24.9000 310311 226 105627 958 2732 157.600503.000 5.20000 12.1800 20552 645 401 25.0000 308746 234 103518 461 3836 161.300

S256 S265 S274 S283 S310 S319 S346 S355 S373 S400 S517 S535 S553 S679

103.200 1473 11.1000 82.0000 2777 51.1900 20 119 60 155 8605 8.20000 613 73105.300 1435 14.0000 81.8000 2827 56.6400 21 113 90 155 12337 7.90000 696 144107.200 1509 15.9000 81.4000 2864 56.9600 23 110 104 159 11647 9.60000 402 149108.500 1689 17.1000 81.1000 2924 60.4100 24 105 101 159 9730 8.60000 285 155110.900 1850 14.9000 80.7000 2980 60.8700 27 98 78 157 9462 9.39999 1215 147

S724 S778 S805 S814 S823 S832 S886 S895 S922 S949 S967 S976

1677 108.100 77123 118.500 184.300 112.460 102.900 103.100 1361 2131 3.00000 6051829 110.600 77347 118.100 185.900 112.990 103.000 101.700 1278 1910 2.80000 5981998 111.600 78986 117.400 186.500 114.280 111.600 105.000 1443 2486 2.80000 5931903 112.200 79429 119.400 185.500 114.890 120.300 107.100 1524 1953 2.90000 5851880 117.200 79241 118.500 187.000 115.580 123.900 115.700 1483 1927 2.90000 582


Sashelp.zhThe Sashelp.zh data set provides DBCS translation tables. ZH is for Chinese. The following steps displayinformation about the data set Sashelp.zh and create Figure 1.100. The data set contains 7,445 observations.

title "Sashelp.zh";proc contents data=sashelp.zh varnum;


title "The First Five Observations Out of 7445";proc print data=sashelp.zh(obs=5) heading=h noobs;run;

Figure 1.100 Sashelp.zh

Sashelp.zh



# Variable Type Len

1 unicode Char 62 ibm Char 43 pcms Char 4


unicode ibm pcms

U+3000 4040 A1A1U+3001 4344 A1A2U+3002 4341 A1A3U+30FB 4345 A1A4U+02C9 4545 A1A5

Sashelp.zipcode — US Zipcodes; Source: Zipcodedownload.com Jul 2013, SAS Release 9.4 F 185

Sashelp.zipcode — US Zipcodes; Source:Zipcodedownload.com Jul 2013, SAS Release 9.4The Sashelp.zipcode data set provides US zip codes. The following steps display information about the dataset Sashelp.zipcode and create Figure 1.101. The data set contains 41,252 observations.

title "Sashelp.zipcode --- US Zipcodes; Source: Zipcodedownload.com Jul 2013, SAS"" Release 9.4";

proc contents data=sashelp.zipcode varnum;ods select position;

run;

title "The First Five Observations Out of 41252";proc print data=sashelp.zipcode(obs=5) heading=h noobs;run;

Figure 1.101 Sashelp.zipcode — US Zipcodes; Source: Zipcodedownload.com Jul 2013, SAS Release 9.4

Sashelp.zipcode --- US Zipcodes; Source: Zipcodedownload.com Jul 2013, SAS Release 9.4




1 CITY2 Char 64 Clean CITY name for geocoding2 STATENAME Char 64 Clean STATENAME for geocoding2

3 ZIP Num 8 Z5. The 5-digit ZIP Code4 Y Num 8 11.6 Latitude (degrees) of the center (centroid) of ZIP Code.5 X Num 8 11.6 Longitude (degrees) of the center (centroid) of ZIP Code.6 ZIP_CLASS Char 1 ZIP Code Classification:P=PO Box U=Unique zip used for

large orgs/businesses/bldgs Blank=Standard/non-unique7 CITY Char 35 Name of city/org8 STATE Num 8 Two-digit number (FIPS code) for state/territory9 STATECODE Char 2 Two-letter abbrev. for state name.

10 STATENAME Char 25 Full name of state/territory11 COUNTY Num 8 FIPS county code.12 COUNTYNM Char 25 Name of county/parish.13 MSA Num 8 Metro Statistical Area code by common

pop-pre 2003; no MSA for rural14 AREACODE Num 8 Single Area Code for ZIP Code.15 AREACODES Char 12 Multiple Area Codes for ZIP Code.16 TIMEZONE Char 9 Time Zone for ZIP Code.17 GMTOFFSET Num 8 Diff (hrs) between GMT and time zone for ZIP Code18 DST Char 1 ZIP Code obeys Daylight Savings: Y-Yes N-No19 PONAME Char 35 USPS Post Office Name: same as City20 ALIAS_ Char 300 USPS - alternate names of city separated by ||

CITY21 ALIAS_ Char 300 Local - alternate names of city separated by ||

CITYN




ZIP_CITY2 STATENAME2 ZIP Y X CLASS CITY STATE STATECODE STATENAME

HOLTSVILLE NEWYORK 00501 40.813078 -73.046388 U Holtsville 36 NY New YorkHOLTSVILLE NEWYORK 00544 40.813223 -73.049288 U Holtsville 36 NY New YorkADJUNTAS PUERTORICO 00601 18.165950 -66.723627 Adjuntas 72 PR Puerto RicoAGUADA PUERTORICO 00602 18.383005 -67.186553 Aguada 72 PR Puerto RicoAGUADILLA PUERTORICO 00603 18.433236 -67.151954 Aguadilla 72 PR Puerto Rico

ALIAS_COUNTY COUNTYNM MSA AREACODE AREACODES TIMEZONE GMTOFFSET DST PONAME CITY

103 Suffolk 5380 631 631 Eastern -5 Y Holtsville103 Suffolk 5380 631 631 Eastern -5 Y Holtsville1 Adjuntas 0 787 787/939 Atlantic -4 N Adjuntas3 Aguada 60 787 787/939 Atlantic -4 N Aguada5 Aguadilla 60 787 787/939 Atlantic -4 N Aguadilla Ramey

ALIAS_CITYN

Colinas Del Gigante||Jard de Adjuntas||Urb San JoaquinAlts de Aguada||Bo Guaniquilla||Comunidad Las Flores||Ext Los Robles||Urb Isabel la CatolicaBda Caban||Bda Esteves||Bo Borinquen||Bo Ceiba Baja||Ext El Prado||Ext Marbella||Repto Jimenez||

Sashelp.zipmil — US Military Zipcodes-Lat/LongNA Assigned Missing F 187

Sashelp.zipmil — US Military Zipcodes-Lat/LongNA AssignedMissingThe Sashelp.zipmil data set provides US military zip codes. The following steps display information aboutthe data set Sashelp.zipmil and create Figure 1.102. The data set contains 534 observations.

title "Sashelp.zipmil --- US Military Zipcodes-Lat/LongNA Assigned Missing";proc contents data=sashelp.zipmil varnum;


title "The First Five Observations Out of 534";proc print data=sashelp.zipmil(obs=5) heading=h noobs;run;

Figure 1.102 Sashelp.zipmil — US Military Zipcodes-Lat/LongNA Assigned Missing

Sashelp.zipmil --- US Military Zipcodes-Lat/LongNA Assigned Missing




1 CITY2 Char 64 Clean CITY name for geocoding2 STATENAME Char 64 Clean STATENAME for geocoding2

3 ZIP Num 8 Z5. The 5-digit ZIP Code4 Y Num 8 11.6 Latitude (degrees) of the center (centroid) of ZIP Code.5 X Num 8 11.6 Longitude (degrees) of the center (centroid) of ZIP Code.6 ZIP_CLASS Char 1 ZIP Code Classification:P=PO Box U=Unique zip used for

large orgs/businesses/bldgs Blank=Standard/non-unique7 CITY Char 35 Name of city/org8 STATE Num 8 Two-digit number (FIPS code) for state/territory9 STATECODE Char 2 Two-letter abbrev. for state name.

10 STATENAME Char 25 Full name of state/territory11 COUNTY Num 8 FIPS county code.12 COUNTYNM Char 25 Name of county/parish.13 MSA Num 8 Metro Statistical Area code by common

pop-pre 2003; no MSA for rural14 AREACODE Num 8 Single Area Code for ZIP Code.15 AREACODES Char 12 Multiple Area Codes for ZIP Code.16 TIMEZONE Char 9 Time Zone for ZIP Code.17 GMTOFFSET Num 8 Diff (hrs) between GMT and time zone for ZIP Code18 DST Char 1 ZIP Code obeys Daylight Savings: Y-Yes N-No19 PONAME Char 35 USPS Post Office Name: same as City20 ALIAS_ Char 300 USPS - alternate names of city separated by ||

CITY21 ALIAS_ Char 300 Local - alternate names of city separated by ||

CITYN




ZIP_CITY2 STATENAME2 ZIP Y X CLASS CITY STATE STATECODE STATENAME

APO NEWYORK 09002 . . M APO 36 NY New YorkAPO NEWYORK 09003 . . M APO 36 NY New YorkAPO NEWYORK 09004 . . M APO 36 NY New YorkAPO NEWYORK 09005 . . M APO 36 NY New YorkAPO NEWYORK 09006 . . M APO 36 NY New York

ALIAS_ ALIAS_COUNTY COUNTYNM MSA AREACODE AREACODES TIMEZONE GMTOFFSET DST PONAME CITY CITYN

. none . . . N APO

. none . . . N APO

. none . . . N APO

. none . . . N APO

. none . . . N APO

Sashelp.zt F 189

Sashelp.ztThe Sashelp.zt data set provides DBCS translation tables. ZT is for traditional Chinese. The following stepsdisplay information about the data set Sashelp.zt and create Figure 1.103. The data set contains 13,459observations.

title "Sashelp.zt";proc contents data=sashelp.zt varnum;


title "The First Five Observations Out of 13459";proc print data=sashelp.zt(obs=5) heading=h noobs;run;

Figure 1.103 Sashelp.zt

Sashelp.zt



# Variable Type Len

1 unicode Char 62 euc Char 43 ibm Char 44 hp15 Char 45 pcibm Char 46 pcms Char 4


unicode euc ibm hp15 pcibm pcms

U+3000 A1A1 4040 F5D1 8140 A140U+FF0C A1A2 426B F5DD 8143 A141U+3001 A1A3 4344 2020 8141 A142U+3002 A1A4 4341 2020 8142 A143U+FF0E A1A5 424B 2020 8144 A144


References

Fisher, R. A. (1936), “The Use of Multiple Measurements in Taxonomic Problems,” Annals of Eugenics, 7,179–188.

Zou, H. and Hastie, T. (2005), “Regularization and Variable Selection via the Elastic Net,” Journal of theRoyal Statistical Society, Series B, 67, 301–320.

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