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© TLC, SS0 070402

Thomas A. Little Ph.D. 07/07/07

JMP 7 and Minitab 15JMP 7 and Minitab 15

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AudienceAudience

762 North 470 EastAmerican Fork, UT 840031-925-285-1847drlittle@dr-tom.comwww.dr-tom.com

DescriptionThis presentation is designed for those individuals who are interested in understanding the differences in the design, function and capabilities of JMP 7 versus Minitab 15. Particular attention is made to those features and functions used for Six Sigma/Lean project application.

Software

JMP 7 and Minitab 15.

Limitations

This presentation is limited to those features and functions of greatest interest to users in the scientific, business, engineering and six sigma/lean communities. An attempt was made to review the features and functions in both applications from a user’s perspective. TLC actively consults with both applications and finds features and functions in both applications that are best in class. Any disagreements about observations found in this presentation should be addressed to the author who welcomes opposing points of view.

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Presentation OutlinePresentation Outline

Section I General Interface and Ease of Use

Section II Lean Six Sigma Activities

Define

Measure

Analyze

Improve

Control

Section III Extended Capabilities

Section IV New Features and Conclusions

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JMP Version 7.0 OverviewJMP Version 7.0 Overview

Power JMP provides more analytical tools, graphs, depth, scripting and

features that are used to solve real world problems Static and dynamic visualization of data via meaningful graphs and

options. Version 7 added significantly to this capability. JMP is particularly good at large data sets and multivariate modeling JMP benefits from SAS’s core capabilities and years of development JMP version 7 improves linkage and data transfer to SAS

Speed Single define, multiple output All graphs and reports in the same window, powerful table commands

not available in excel Control, command function to manipulate them all

Ease of Use JMP organization simplifies the windows, text and graphs integrated Simplified interface to complex activities such as Fit Y by X and Fit

Model Ease of data and table manipulation.

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Minitab Version 15Minitab Version 15

Both Minitab (MT) and JMP are far superior for data analysis than using Excel

MT is a mature, full featured product with years of user input and product features

MT was selected by GE and Honeywell as the early six sigma engine of choice when JMP was just developing version 4. At the time they were correct, MT was the better, more mature product. The world has spun since that time and JMP has surpassed MT’s capabilities in all three of the areas of greatest interest to users; speed, power and ease of use.

MT release 15 remains a blessing and a curse. Blessing due to its years of application development and familiar tools. Curse due to its old, awkward interface and software design.

MT continues to be a much slower application once the data sets rises above 100,000 observations.

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Section ISection I

General Interface and Ease of Use

General design

Windows

Organization

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General Design, Tables General Design, Tables

Minitab uses projects and worksheets as major file formats; where projects are collections of worksheets. JMP has similar capabilities.

Table commands for Minitab and JMP are very similar and JMP has some additional table features not found in MT.

More table manipulation tools in JMP and more readable file formats.

Advantage JMP

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Data Table SizeData Table Size

Opening and Manipulating Large Data Sets*

File Size (rows) Time to File Open Time to Display One Histogram

JMP Minitab JMP Minitab

1 M <1 sec. 13 sec. 1 sec. 90 sec.

5 M 5 sec. 15 sec. 6 sec. 100 sec.

20 M 24 sec. Failed. 35 sec. Failed to display

Minitab failed to load 20M rows, all 3 columns, only one column loaded.

Advantage JMP

JMP takes seconds and Minitab takes minutes to manipulate data. If datasets are large as they are in many transactional environments MT is not a tenable solution. Even with moderately sized data tables MT feels slow on response times.

*MT JMP evaluation PC used was running Vista, 1.80 GHz Duo, 2GB RAM

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Data Tables and Graphs Linked Data Tables and Graphs Linked

In MT there is row identification capability; however, no real connection between the graph and table.

JMP makes the connection which allows for ease of row location, data and graph manipulation.

Major Advantage JMP

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MenusMenus

MT displays the analysis method by name.

JMP layers the analysis based on one variable, two, paried and multiple Xs and multiple Ys.

Menu Pros and Cons

Minitab is easier to use if you are looking for a specific type of analysis by name.

JMP’s Analyze tools are organized based on single, two, paired and multiple factors. JMP is generalized and easier to learn and remember. This is particularly true of Green Belt level training.

Major Advantage JMP

Analysis of One

Two

Paired or

Many variables of any data type.

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Graphs and AnalysisGraphs and Analysis

Minitab uses a separate graph and session window for most of the output. This feature is very annoying in Minitab and slows down the user and the time to analysis understanding. It is a very old school design.

JMP keeps all reports and graphs together in one place.

Advantage JMP

File: Clean.

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SubsetsSubsets

JMP is visual and intuitive when creating subsets. MT does it with formulas, row numbers or brushing.

Advantage JMP

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Formulas and FunctionsFormulas and Functions

JMP has a complete and rich set of integrated functions for data and string manipulation. MT has fewer overall functions and they are spread out and segmented in the Calc function.

Advantage JMP

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Section IISection II

Six Sigma Activities

Define Link to process flow analysis

Measure Process capability and MSA

Analyze Hypothesis testing and performance modeling

Improve Design of Experiments and Robust Tolerance Design

Control SPC

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Define, Process Flow AnalysisDefine, Process Flow Analysis

Minitab and JMP are developing partnerships for linking process mapping, value stream mapping and Lean manufacturing analysis tools into their respective analytical engines. iGrafx for example has both JMP and MT connections.

Advantage - Draw

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Indiv

idual V

alu

e

180160140120100806040201

180

170

160

_X=170.62

UCL=177.95

LCL=163.30

Movin

g R

ange

180160140120100806040201

10

5

0

__MR=2.76

UCL=9.00

LCL=0

Observation

Valu

es

195190185180175

180

175

170

180177174171168165162

180170160

Within

Overall

Specs

WithinStDev 2.44247Cp 1.09Cpk 1.01CCpk 1.09

OverallStDev 3.99757Pp 0.67Ppk 0.62Cpm *

1

11111

1111

111

1

1

11

1

11

11

Process Capability Sixpack of CnI Chart

Moving Range Chart

Last 25 Observations

Capability Histogram

Normal Prob PlotAD: 0.666, P: 0.081

Capability Plot

Process Capability, Minitab NormalProcess Capability, Minitab Normal

File: Cn

MT’s process potential study is poorly named in this graph. Missing PPM and sigma quality.

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Process Capability, JMP NormalProcess Capability, JMP Normal

160

165

170

175

180

185

Cn

11

11

111

11 1 1

11 1 11

1

15 30 45 60 75 90 120 150 180 210

Sample

Avg=170.62

LCL=163.30

UCL=177.95

Individual Measurement of Cn

0

10

Mov

ing

Ran

ge o

f Cn

* *

**

* **

15 30 45 60 75 90 120 150 180 210

Sample

Avg=2.76

LCL=0.00

UCL=9.00

Moving Range of Cn

.01

.05

.10

.25

.50

.75

.90

.95

.99

-3

-2

-1

0

1

2

3

Nor

mal

Qua

ntile

Plo

t

LSL USLTarget

10

20

30

40

Cou

nt

160 165 170 175 180

Normal(170.624,3.99247)

Lower Spec LimitUpper Spec LimitSpec Target

Specification162178170

Value Below LSLAbove USLTotal Outside

Portion0.50762.53813.0457

% Actual

LSL USLTarget

-3s +3sMean

160 170 180

CPCPKCPMCPLCPU

Capability0.6680.6160.6600.7200.616

Index0.6020.5390.5960.6350.539

Lower CI0.7340.6920.7240.8050.692

Upper CI

Below LSLAbove USLTotal Outside

Portion1.53803.23464.7726

Percent15380.21232345.56247725.774

PPM3.6603.3473.167

Sigma Quality

Z BenchZ LSLZ USL

Benchmark Z1.6672.1601.847

Index

Overall, Sigma = 3.99247

LSL USLTarget

-3s +3sMean

160 170 180

CPCPKCPMCPLCPU

Capability1.0921.0071.0581.1771.007

Index0.9840.8970.9571.0520.897

Lower CI1.2001.1171.1601.3031.117

Upper CI

Below LSLAbove USLTotal Outside

Portion0.02060.12610.1467

Percent206.0636

1260.69011466.7537

PPM5.0324.5214.475

Sigma Quality

Z BenchZ LSLZ USL

Benchmark Z2.9753.5323.021

Index

Control Chart, Sigma = 2.44165

Capability Analysis

LocationDispersion

Typeµs

Parameter170.624373.9924693

Estimate170.063393.6333224

Lower 95%171.185344.4310244

Upper 95%

Parameter Estimates

Fitted Normal

Cn

Distributions

Control Chart

JMP’s second capability graph is poorly named. It should be called process potential.

JMP’s six graph analysis is hard to find without training; however, it is very good and is easy to interact with. It is a feature under control charts. JMP includes sigma quality in its report and has more secondary options. It allows for nonnormal fit selection on the fly. Advantage JMP

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USL

20

40

60

Co

un

t

10 20

Gamma(3.79285,1.71318,0)

100.0%99.5%97.5%90.0%75.0%50.0%25.0%10.0%

2.5%0.5%0.0%

maximum

quartilemedianquartile

minimum

17.91017.39714.69111.3228.2455.9593.9282.766

1.7571.3781.209

Quantiles

MeanStd DevStd Err Meanupper 95% Meanlower 95% MeanNSum WgtSum

VarianceSkewnessKurtosisCVN Missing

6.49783443.353378

0.15057116.79367176.2019971

496496

3222.9258

11.2451440.86783860.457376851.607625

0

Moments

ShapeScaleThreshold

Typeas?

Parameter3.79285091.7131795

0

Estimate3.35854881.5120833

.

Lower 95%4.26458931.9521407

.

Upper 95%

Note: Unable to converge on all confidence limits.

Parameter Estimates

Ga

mm

a Q

ua

ntil

e

01

3

5

7

9

11

0 5 10 15 20

Particles

Quantile Plot

Lower Spec LimitUpper Spec LimitSpec Target

Specification.

20.

Value %Below LSL%Above USL

Percent.

0.000

Actual

USL

-3s +3sMean

0 10 20

CP

CPKCPMCPLCPU

Capability.

0.928..

0.928

Index

Below LSLAbove USLTotal Outside

Portion.

0.22360.2236

Percent.

2235.61882235.6188

PPM.

4.3434.343

Sigma Quality

Overall, Sigma = 3.33646

Capability Analysis

Fitted Gamma

Particles

Distributions

Nonnormal Capability FittingNonnormal Capability Fitting

File: Skewed

JMP and MT have similar fitting capabilities, JMP has an interactive interface and an overall better report.

Advantage JMP

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Nonnormal Capability in MinitabNonnormal Capability in Minitab

18.014.410.87.23.60.0

USLProcess Data

Sample N 496Shape 3.74418Scale 1.73710

LSL *Target *USL 20.00000Sample Mean 6.50403

Overall CapabilityPp *PPL *PPU 0.92Ppk 0.92

Observed PerformancePPM < LSL *PPM > USL 0PPM Total 0

Exp. Overall PerformancePPM < LSL *PPM > USL 2373.00PPM Total 2373.00

Process Capability of Particles_1Calculations Based on Gamma Distribution Model

MT is missing the sigma quality level and the quantile plot to look at the quality of the fit. The sixpack report is a better option in general when using MT.

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Minitab ParetoMinitab Pareto

Count

Perc

ent

Causes

Count41.0 32.1 6.7 6.3 6.0 4.1 3.7

Cum % 41.0 73.1

110

79.9 86.2 92.2 96.3 100.0

86 18 17 16 11 10Percent

Dopin

g

Metall

izatio

n

Corro

sion

Silicon

Defec

t

Miscell

aneo

us

Oxide

Defec

t

Contam

inatio

n

300

250

200

150

100

50

0

100

80

60

40

20

0

Pareto Chart of Causes

MT does not allow for easy selection of comparison groups and does not allow for DPU summary tables from the Pareto platform. Cannot directly generate a cost or severity weighted Pareto plot.

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JMP ParetoJMP ParetoP

roce

ss A

0

5

10

15

20

25

30

35

Co

un

t

03/01/1991 03/02/1991 03/03/1991 03/04/1991 03/05/1991

010203040

5060708090100

Cu

m P

erc

en

t

Pro

cess

B

0

5

10

15

20

25

30

35

Co

un

t

Co

nta

min

atio

n

Oxi

de

De

fect

Mis

cella

ne

ou

s

Co

rro

sio

n

Me

talli

zatio

n

Do

pin

g

Sili

con

De

fect

Causes

Co

nta

min

atio

n

Oxi

de

De

fect

Mis

cella

ne

ou

s

Co

rro

sio

n

Me

talli

zatio

n

Do

pin

g

Sili

con

De

fect

Causes

Co

nta

min

atio

n

Oxi

de

De

fect

Mis

cella

ne

ou

s

Co

rro

sio

n

Me

talli

zatio

n

Do

pin

g

Sili

con

De

fect

Causes

Co

nta

min

atio

n

Oxi

de

De

fect

Mis

cella

ne

ou

s

Co

rro

sio

n

Me

talli

zatio

n

Do

pin

g

Sili

con

De

fect

Causes

Co

nta

min

atio

n

Oxi

de

De

fect

Mis

cella

ne

ou

s

Co

rro

sio

n

Me

talli

zatio

n

Do

pin

g

Sili

con

De

fect

Causes

010203040

5060708090100

Cu

m P

erc

en

t

Plots

0

50

100

150

200

250

Cou

nt

Con

tam

inat

ion

Oxi

de D

efec

t

Mis

cella

neou

s

Sili

con

Def

ect

Cor

rosi

on

Met

alliz

atio

n

Dop

ing

Causes

0

10

20

30

40

50

60

70

80

90

100

Cum

Per

cent

Plots

Sample Size = 26488

ContaminationOxide DefectMiscellaneousSilicon DefectCorrosionMetallizationDopingPooled Total

Cause110861817161110

268

Count0.00420.00320.00070.00060.00060.00040.00040.0014

DPU0.00340.00260.00040.00040.00030.00020.00020.0013

Lower 95%0.00500.00400.00110.00100.00100.00070.00070.0016

Upper 95%

Per Unit Rates

JMP allows for easy grouping variables, DPU summary tables and cost and severity weighted Pareto generation. Advantage JMP

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Surface Plots, MTSurface Plots, MT

vph

tpd

11109876

0.00000010

0.00000009

0.00000008

0.00000007

0.00000006

0.00000005

0.00000004

0.00000003

0.00000002

0.00000001

Yield

0.1 - 0.20.2 - 0.30.3 - 0.40.4 - 0.50.5 - 0.6

<

0.6 - 0.70.7 - 0.80.8 -

0.0

0.90.9 - 1.0

> 1.0

0.0 - 0.1

Contour Plot of Yield vs tpd, vph

Yield

0.0 0.00000000

0.5

1.0

10.5 0.000000059.0 tpd

7.5vph 0.000000106.0

Surface Plot of Yield vs tpd, vph

Both MT and JMP have nice surface characterization capabilities. MT is slow to generate and difficult to manipulate. Control over the image is slower and has less options.

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Surface Plots, JMPSurface Plots, JMP

6.0

7.0

8.0

9.0

10.0

11.0

vph

1e-8 2e-8 3e-8 4e-8 5e-8 6e-8 7e-8 8e-8 9e-8

tpd

3D visualization in JMP is excellent in either the contour or surface plots. JMP allows for up to 100 gradients and MT allows for only 11 in the contour plot. JMP’s Surface Profiler is based on Open GL a full 3D graphics engine.

Advantage JMP

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GR&R in MTGR&R in MT

ANOVA analysis is similar, JMP has the variability graph which is better at displaying variation patterns. MT removes some of the misleading AIAG reports and provides an easier to read report format. MT is missing the secondary breakdown of variation.

File: Gage study

Per

cent

Part-to-PartReprodRepeatGage R&R

80

40

0

% Contribution

% Study Var

% Process% Tolerance

Sam

ple

Ran

ge

1.0

0.5

0.0

_R=0.113

UCL=0.292

LCL=0

Cindy George Tom

Sam

ple

Mea

n

1.00

0.75

0.50

__X=0.8106

UCL=0.9265

LCL=0.6946

Cindy George Tom

Part10987654321

1.5

1.0

0.5

OperatorTomGeorgeCindy

1.5

1.0

0.5

Part

Ave

rage

10 9 8 7 6 5 4 3 2 1

1.00

0.75

0.50

Operator

Cindy

GeorgeTom

Gage name:Date of study:

Reported by:Tolerance:Misc:

Components of Variation

R Chart by Operator

Xbar Chart by Operator

Measurement by Part

Measurement by Operator

Operator * Part Interaction

Gage R&R (ANOVA) for Measurement

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RepeatabilityOperator*PartReproducibilityGage R&RPart VariationTotal Variation

Measurement0.57514740.29182400.34547250.67092900.81644501.0567536

Variation28.7614.5917.2733.5540.8252.84

% of Tolerance31.9116.1919.1737.2245.3058.63

% ProcessV(Within)V(Operator*Part)V(Operator)+V(Operator*Part)V(Within)+V(Operator)+V(Operator*Part)V(Part)V(Within)+V(Operator)+V(Operator*Part)+V(Part)

which is k*sqrt of

5.1563.48960.82177

12

0.335460.35

k% Gage R&R = 100*(RR/TV)Precision to Part Variation = RR/PVNumber of Distinct Categories = 1.41(PV/RR)Tolerance = USL-LSLPrecision/Tolerance Ratio = RR/(USL-LSL)Historical Sigma

Gage R&R Repeatability ReproducibilityPart-to-Part

Component0.016972220.012472220.004500000.02513272

Var Component40.3129.6210.6959.69

% of Total 20 40 60 80

Variance Components for Gage R&R

Gage R&R

JMP GR&R FunctionalityJMP GR&R Functionality

JMP has the variability chart that is better for showing variation patterns in the data; however, it is missing the control chart for outlier detection and the summary graphs. JMP needs to add the control chart, summary graphs and secondary breakdown of the variation patterns to be best in class.

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Bias and Linearity, MTBias and Linearity, MT

Reference Value

Bia

s

1.00.90.80.70.60.5

1.00

0.75

0.50

0.25

0.00

-0.25

-0.50

0

Regression

95% CI

Data

Avg Bias

Perc

ent

BiasLinearity

20

10

0

Gage Linearity

Slope -0.18463 0.07619 0.017

Predictor Coef SE Coef PConstant 0.13379 0.06474 0.042

S 0.131509 R-Sq 6.3%Linearity 0.064619 % Linearity 18.5

Gage Bias

0.5 -0.027778 7.9 0.3390.55 0.111111 31.7 0.2910.8 -0.018056

Reference

5.2 0.2260.95 -0.044444 12.7 0.144

1 0.011111 3.2 0.516

Bias

1.05 -0.086111 24.6 0.003

% Bias PAverage -0.019444 5.6 0.090

Gage name:Date of study:

Reported by:Tolerance:Misc:

Percent of Process Variation

Gage Linearity and Bias Study for Measurement The linearity graph in MT is in error. The reference line should be relative to the mean and not to zero.

MT does not have the secondary breakdown of bias by part and by comparison group.

MT does have the p-values for all of the comparisons which is very desirable.

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Bias and Linearity, JMPBias and Linearity, JMP

JMP’s reports are correct and more detailed in general. JMP is missing the p-values for the bias errors. JMP displays the impact to the standard deviation based on rotation effects.

Advantage JMP

Bia

s/A

ccur

acy

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Cindy George Tom

Operator

CindyGeorgeTom

Operator0.02500

-0.01833-0.06500

Avg Bias

Bias Report for Operator

Bia

s/A

ccur

acy

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1 2 3 4 5 6 7 8 9 10

Part

12345678910

Part0.111110.011110.011110.02778

-0.02778-0.08889-0.044440.00000

-0.08333-0.11111

Avg Bias

Bias Report for Part

Bia

s/A

ccu

racy

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

.5 .6 .7 .8 .9 1.0 1.1

Standard Value

Measurement = 0.1337949 - 0.1846257 Standard Value

0.50000

0.550000.800000.950001.00000

1.05000

Standard Value0.47222

0.661110.781940.905561.01111

0.96389

Avg Response-0.02778

0.11111-0.01806-0.044440.01111

-0.08611

Avg Bias-0.43030

-0.37941-0.22487-0.32794-0.38733

-0.45229

Lower CL0.513260

0.4439130.1970550.2447450.285671

0.332170

Upper CL

Linearity% LinearityAvg Bias/Accuracy

% AccuracyProcess Variationt RatioProb>|t|

R-Squared

-0.06518.463

-0.00903

2.5790.350

-2.4230.017

0.082

Slope * Process Variation100 * abs(Slope)Bias averaged over all parts

100 * AvgBias / Process VariationEntered on dialogtests H0: the slope equals 0small pvalues = slope is not likely 0

Which equals

Linearity Study

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Attribute GR&R, MTAttribute GR&R, MT

Appraiser

Perc

ent

MariaJ uanErnesto

100

90

80

70

60

50

95.0%  CIPercent

Appraiser

Perc

ent

MariaJ uanErnesto

100

90

80

70

60

50

95.0%  CIPercent

Date of study: Reported by:Name of product:Misc:

Assessment Agreement

Within Appraisers Appraiser vs Standard

MT has a very good and very detailed agreement analysis report; however, it is poor on graphing and labeling of effectiveness. Agreement/effectiveness by part, prob(miss), prob(false alarm), bias report and escape rate are all missing in MT.

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Attribute GR&R, JMPAttribute GR&R, JMP

% A

gree

men

t

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 1011 121314 15 16 171819 20 21222324 25 262728 2930

Part No.

% A

gree

men

t

20

40

60

80

100

Juan Maria Ernesto

Rater

Agreement between & within ratersEffectiveness (Agreement to Standard)

JuanMariaErnesto

Rater68.888976.666774.4444

% Agreement51.000559.071756.7146

95% Lower CI82.489088.207686.6248

95% Upper CI

30Number Inspected

16Number Matched

53.333% Agreement

36.14295% Lower CI

69.76895% Upper CI

Agreement Report

JuanMariaErnesto

Rater303030

Number Inspected282829

Number Matched93.333393.333396.6667

Rater Score78.676578.676583.3296

95% Lower CI98.152398.152399.4091

95% Upper CI

Agreement within Raters

JuanMariaErnesto

Rater253834

Correct(0)415042

Correct(1)668876

Total Correct1415

Incorrect(0)1019

Incorrect(1)909090

Grand Total

Agreement Counts

JuanMariaErnesto

Rater73.333397.777884.4444

Effectiveness63.380292.255575.5672

95% Lower CI81.376299.388590.5017

95% Upper CI0.26670.02220.1556

Error rate

Effectiveness

01Other

Standard Level.

200

020

.0

1

Misclassifications

JuanMariaErnesto

Rater0.19610.01960.1765

P(False Alarms)0.35900.02560.1282

P(Misses)NonConform = Conform =

Assumptions01

Conformance Report

Effectiveness Report

Gage Attribute Chart

Attribute Gage

% A

gree

men

t

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 1011 121314 15 16 171819 20 21222324 25 262728 2930

Part No.

% A

gree

men

t

20

40

60

80

100

Juan Maria Ernesto

Rater

Agreement between & within ratersEffectiveness (Agreement to Standard)

JuanMariaErnesto

Rater68.888976.666774.4444

% Agreement51.000559.071756.7146

95% Lower CI82.489088.207686.6248

95% Upper CI

30Number Inspected

16Number Matched

53.333% Agreement

36.14295% Lower CI

69.76895% Upper CI

Agreement Report

JuanMariaErnesto

Rater303030

Number Inspected282829

Number Matched93.333393.333396.6667

Rater Score78.676578.676583.3296

95% Lower CI98.152398.152399.4091

95% Upper CI

Agreement within Raters

JuanMariaErnesto

Rater253834

Correct(0)415042

Correct(1)668876

Total Correct1415

Incorrect(0)1019

Incorrect(1)909090

Grand Total

Agreement Counts

JuanMariaErnesto

Rater73.333397.777884.4444

Effectiveness63.380292.255575.5672

95% Lower CI81.376299.388590.5017

95% Upper CI0.26670.02220.1556

Error rate

Effectiveness

01Other

Standard Level.

200

020

.0

1

Misclassifications

JuanMariaErnesto

Rater0.19610.01960.1765

P(False Alarms)0.35900.02560.1282

P(Misses)NonConform = Conform =

Assumptions01

Conformance Report

Effectiveness Report

Gage Attribute Chart

Attribute Gage

JMP’s attribute GR&R report is very good and covers agreement and effectiveness very well. It is missing bias and escape rate. JMP’s graphs are better at showing agreement (blue line) and effectiveness (red line).

Advantage JMP

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Context Sensitive Fit Y by XContext Sensitive Fit Y by X

This is where JMP shines over Minitab and provides the user with the proper analysis depending on the data type. JMP automatically switches between four different analytical platforms depending on the column attributes.

Advantage JMP

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Correlation Fit Y by XCorrelation Fit Y by X

Correlation studies, exploratory data analysis, fit special, group by, etc., this is where JMP outperforms MT on option after option.

Advantage JMPFile: Factory RSM

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Cau

ses

0.00

0.25

0.50

0.75

1.00

Process A Process B

Process

Contamination

CorrosionDopingMetallizationMiscellaneous

Oxide Defect

Silicon Defect

Mosaic Plot

Freq: Failure Count

Pro

cess

Process A

Process B

8632.09

82.99

51.87

51.87

82.99

4215.67

82.99

248.96

82.99

51.87

62.24

103.73

4416.42

93.36

16260.45

10639.55

11041.04

165.97

103.73

114.10

186.72

8632.09

176.34

268

CausesCountTotal %

Contamination Corrosion Doping Metallization Miscellaneous Oxide Defect Silicon Defect

Contingency Table

ModelErrorC. TotalN

Source6

256262268

DF12.85597

391.44640404.30237

-LogLike0.0318

RSquare (U)

Likelihood RatioPearson

Test25.71224.743

ChiSquare0.0003*0.0004*

Prob>ChiSq

Tests

Contingency Analysis of Causes By Process

Fit Y by X Contingency TablesFit Y by X Contingency Tables

JMP and MT have similar summary table capabilities; however, MT is missing the visualization graphs.

Advantage JMP

File: Failures

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Multiple Regression, N-Way, ANCOVAMultiple Regression, N-Way, ANCOVA

MT requires detailed statistical and modeling training to remember the names of all of the types of ANOVA. Once the analysis is preformed there is not an easy to use suite of tools and secondary graphs for the user to interact with for further visualization, characterization and optimization. Tools are segmented and not well integrated for optimization.

File: cement

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Multiple Regression, N-Way, ANCOVAMultiple Regression, N-Way, ANCOVA

File: cement

20

25

30

35

Str

en

gth

Ac

tua

l

20 25 30 35

Strength Predicted P<.0001RSq=0.82 RMSE=1.7691

Actual by Predicted Plot

RSquareRSquare AdjRoot Mean Square ErrorMean of ResponseObservations (or Sum Wgts)

0.8156220.7326521.76906325.99761

30

Summary of Fit

ModelErrorC. Total

Source9

2029

DF276.8826262.59167

339.47429

Sum of Squares30.76473.1296

Mean Square9.8303F Ratio

<.0001*Prob > F

Analysis of Variance

InterceptBrand[Consolidated]Brand[EZ Mix]Additive[reinforced]HumidityBrand[Consolidated]*Additive[reinforced]Brand[EZ Mix]*Additive[reinforced]Brand[Consolidated]*(Humidity-51.01)Brand[EZ Mix]*(Humidity-51.01)Additive[reinforced]*(Humidity-51.01)

Term39.078555-1.567535-0.89043

1.2358601-0.254865-0.21502

-0.5909180.0187235-0.1056330.0848815

Estimate3.6708830.4736120.5024410.3758340.0737240.5132780.5514370.0941790.0861850.07312

Std Error10.65-3.31-1.773.29

-3.46-0.42-1.070.20

-1.231.16

t Ratio<.0001*0.0035*0.09160.0037*0.0025*0.67970.29670.84440.23460.2594

Prob>|t|

Parameter Estimates

BrandAdditiveHumidityBrand*AdditiveBrand*HumidityAdditive*Humidity

Source211221

Nparm211221

DF84.83916533.84023137.4010907.3667225.8157254.217313

Sum of Squares13.554410.813011.95081.17690.92921.3476

F Ratio0.0002*0.0037*0.0025*0.32870.41130.2594

Prob > F

Effect Tests

-3

-2

-1

0

1

2

3

4

Str

en

gth

Re

sid

ua

l

20 25 30 35

Strength Predicted

Residual by Predicted Plot

Whole Model

20

25

30

35

Str

en

gth

Le

ve

rag

e R

es

idu

als

24 25 26 27 28 29 30

Brand Leverage, P=0.0002

Leverage Plot

ConsolidatedEZ MixGraystone

Level24.51034425.18744928.535844

Least Sq Mean0.581743660.689515280.60951464

Std Error24.201125.823727.9681

Mean

Least Squares Means Table

Brand

20

25

30

35

Str

en

gth

Le

ve

rag

e R

es

idu

als

24.5 25.5 26.5 27.5 28.5

Additive Leverage, P=0.0037

Leverage Plot

reinforcedstandard

Level27.31373924.842019

Least Sq Mean0.584837410.50664460

Std Error27.904024.0912

Mean

Least Squares Means Table

Additive

20

25

30

35

Str

en

gth

Le

ve

rag

e R

es

idu

als

40 45 50 55 60 65 70

Humidity Leverage, P=0.0025

Leverage Plot

Humidity

20

25

30

35

Str

en

gth

Le

ve

rag

e R

es

idu

als

23 24 25 26 27 28 29 30 31

Brand*AdditiveLeverage, P=0.3287

Leverage Plot

Consolidated,reinforcedConsolidated,standardEZ Mix,reinforcedEZ Mix,standardGraystone,reinforcedGraystone,standard

Level25.53118423.48950425.83239224.54250730.57764226.494047

Least Sq Mean0.84485470.86608031.14017510.79189900.80311390.9428028

Std Error

Least Squares Means Table

Brand*Additive

20

25

30

35

Str

en

gth

Le

ve

rag

e R

es

idu

als

25.0 25.5 26.0 26.5 27.0 27.5

Brand*HumidityLeverage, P=0.4113

Leverage Plot

Brand*Humidity

20

25

30

35

Str

en

gth

Le

ve

rag

e R

es

idu

als

25.5 26.0 26.5 27.0

Additive*HumidityLeverage, P=0.2594

Leverage Plot

Additive*Humidity

Response Strength

20

25

30

35

Str

engt

h25

.531

18±1

.762

336

0.00

0.50

1.00

Des

irabi

lity

0.46

2776

Con

solid

ated

EZ

Mix

Gra

ysto

ne

ConsolidatedBrand

rein

forc

ed

stan

dard

reinforcedAdditive

40 45 50 55 60 65 70

51.01Humidity

.00

.25

.50

.75

1.00

Desirability

Prediction Profiler

In addition to the detailed statistical summary tables JMP offers a full suite of graphs for visualization, characterization and optimization. Advantage JMP

Simple model definition no matter the data type.

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Design of Experiments - DesignDesign of Experiments - Design

DOE in Minitab is awkward to use for designing experiments as it does not allow for the direct design of the experiment in line with the problem that needs characterization.

Minitab uses a candidate points method for customization and augmentation. This is very old school and tedious for the user. Covariates are not part of the design, they are secondary in the analysis.

Minitab does not allow for correct factor identification when designing the experiment. There are many more factor types than those allowed by MT. MT fails the ease of use test for DOE.

File: Yield

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DOE Analysis, MTDOE Analysis, MT

MT’s analysis tools for DOE are segmented, do not flow well and the optimizer is missing a more intuitive set of controls for constraining, fixing, optimizing and predicting the response. MT’s DOE design and analysis flow is segmented, complicated, not seamlessly integrated and has too many steps.

Analysis flow

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Design of Experiments in JMPDesign of Experiments in JMP

JMP custom designs match the problem. Any combination of factors, factor types, covariates, blocking sizes, categorical factors and mixtures with a minimum sample size. Simple to define the model terms to be characterized. Allows the most flexible environment for DOE treating the engineer and scientist as the customer.

JMP is best is class for DOE. JMP wins on DOE ease of use.

In JMP the DOE design always fits the problem.

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DOE Analysis in JMP is the Same Fit Model EngineDOE Analysis in JMP is the Same Fit Model Engine

In JMP learn one set of tools and use them for a variety of characterization, DOE, modeling problem solving activities. JMP’s profiler allows for improved visualization and control of the transfer functions. Major Advantage JMP

800

1200

1600

Out

put

1275

±20.

7491

5

2.35

2.45

2.55

2.65

Dia

met

er2.

499

±0.0

2593

6

0

5

10

15

20

Cra

cks

4.8

±1.0

3745

7

0.00

0.50

1.00

Des

irabi

lity

0.05

3217

100

125

150

175

200

150Speed

250

260

270

280

290

300

275Temp

5 6 7 8 9 10

7.5Time

15 20 25 3022.5

Pressure.0

0

.25

.50

.75

1.00

Desirability

Prediction Profiler

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JMP’s Simulator Linked to Transfer FunctionsJMP’s Simulator Linked to Transfer Functions

Optimize performance, improve robustness and predict full distribution at target. MT does not have this capability. Set and evaluate tolerances.

Major Advantage JMP

LSL USL

25000

50000

75000

Co

un

t

2.48 2.5 2.52 2.54 2.56 2.58 2.6

Lower Spec LimitUpper Spec LimitSpec Target

Specification2.512.57

.

Value Below LSLAbove USLTotal Outside

Portion0.40500.72751.1325

% Actual

LSL USL

-3s +3sMean

2.48 2.52 2.56 2.6

CPCPKCPMCPLCPU

Capability0.8490.846

.0.8530.846

Index0.0000.844

.0.8510.844

Lower CI0.8500.847

.0.8540.847

Upper CI

Below LSLAbove USLTotal Outside

Portion0.52710.55861.0857

Percent5270.62105586.212510856.834

PPM4.0584.0373.795

Sigma Quality

Z BenchZ LSLZ USL

Benchmark Z2.2952.5582.537

Index

Overall, Sigma = 0.01178

Capability Analysis

Diameter

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Power and Sample SizePower and Sample Size

JMP has sample size calculation for counts per unit and for estimating the standard deviation. MT identifies sample size for replicates for two specific forms of DOE and JMP does not. JMP also has a sigma quality converter and calculator.

Minor Advantage JMP

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SPCSPC

Advantage - Draw

MT and JMP’s capabilities are quite similar. MT offers more charts; however, JMP’s charts are easier to manipulate and are better for larger data sets. JMP needs to add the short run Z and delta to target charts. Both platforms allow for phased control charts to show before and after effects.

JMP 6 to Minitab 14 Comparison 11/22/2005

SPC Control Charts JMP 6.0 MT 14.1

Control Charts for SubgroupsXbar R Y YXbar S Y YPresummarize Y YDelta to Target, subgroup N NZ subgroup N Y

Control Charts for IndividualsRun Chart Y YI/MR Y YZ/MR individual N YDelta to Target, individual N NLevey Jennings Y N

Control Charts for Small Mean ShiftsUWMA (moving average) Y YEWMA Y Y

CUSUM Y Y

Control Charts for AttributesP Y YNP Y Y

C Y YU Y Y

Multivariable Control ChartsT2 N YMultivariate EWMA N Y

© 11/22/05

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Section IIISection III

Extended Capabilities

Reliability

Multivariate

Time Series

Graphs

Advanced Modeling

Summary

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ReliabilityReliability

MT offers reliability planning tools for sample size determination and JMP does not. JMP has stronger modeling and multivariate tools for reliability modeling.

Advantage - Draw

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MultivariateMultivariate

JMP has a richer set of tools for multivariate analysis. Factor analysis and principle components analysis are in the multivariate platform and are harder to locate from the menu.

Advantage JMP

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Time SeriesTime Series

JMP and Minitab similar tools and capabilities. JMP has a few more options and the ease of use and graphical manipulation makes it superior to MT.

Minor Advantage JMP

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GraphsGraphs

JMP offers similar graphs to MT; however, it outperforms in the profiler, contour profiler, surface plot and custom profiler options. MT does not have the same rich tools for optimization and robust design.

Advantage JMP

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Advanced Modeling ToolsAdvanced Modeling Tools

JMP offers a much richer and versatile set of modeling tools and analytical methods. Neural nets, recursive partitions and nonlinear modeling are all available modeling tools in JMP.

Advantage JMP

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For A More Detailed ComparisonFor A More Detailed Comparison

JMP 6 to Minitab 14 Comparison 11/22/2005

Product Features JMP 6.0 MT 14.1

File and Data accessTable design and tools A BSupporting file formats A BLarge data table manipulation (1M rows +) A DDatabase connection A B+Project file management no feature A

CustomizationProgrammability, scripting A BMenus (names and graphics) A AToolbars A AKeyboard commands no feature AFull automation A B

Ease of UseJMP Starter A no featureGraph Manipulation A CMenus A BHelp functions B AContext sensitive help A no featureToolbars A BGraph and data table link A CDocumentation B ADynamic graphs using scripts A no featureIntegrated graphs and reports A CData editing and modification A A

For a more detailed comparison of JMP versus MT take a look at the JMP 6 to MT 14 comparison table.

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SummarySummary

JMP is in general a superior product

JMP is world class for regression, modeling, DOE, and simple studies such as process capability and MSA and the user interface is very well designed

JMP is easier to use, more powerful, much faster in completing analysis of data and needs to address some of the minor gaps identified in this comparison

Having two great applications is good for the market and keeps both applications improving to meet customer needs and expectations

MT is a good application and has a rich set of tools. JMP is a great application and has an overall better designed and better integrated tool set.

Helping companies understand why Excel is not enough for analysis is the greatest opportunity

Minitab must address the ease of use, some missing tools and speed issues.

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© TLC, SS0 070402

762 North 470 East American Fork, UT 84003 925-285-1847 drlittle@dr-tom.com www.dr-tom.com