CH. 1 Graph Graph Graph Graph Graph Graph 1: 2: 3: 4: 5: 6: Scatterplot Time Plot (hardly every used) Histogram Box & Whisker with outliers shown as asterisks Box & Whisker with no outliers shown Normal Probability Plot (linearity means your data is approx. normal) Graphs: STAT PLOT (2nd Y=) Turn on choose graph Clear all equations in Y= and hit ZOOM #9 TRACE & arrow over for specific points

Summary Statistics: STAT CALC #1: 1-Var Stats L1 (or any other list) ENTER Shows mean, median, std. dev., Q 1, Q 3, etc.

CH. 2 Area Under a Normal Curve (instead of looking in the z-table): DISTR (2nd VARS) #2: normalcdf ( specify your interval ENTER Note: Interval must be standardized (in z-scores) & you need lower or upper bounds. Example: Area to the left of 0.4: DISTR #2: normalcdf (-100, 0.4) ENTER = .6554 Area to the right of 0.4: DISTR #2: normalcdf (0.4, 100) ENTER = .3446 Area between -0.4 and 0.4: DISTR #2: normalcdf (-0.4, 0.4) ENTER = .3108 Reverse Z-Score (instead of looking in the table): DISTR (2nd VARS) #3: invNorm ( specify the area in the parentheses ENTER Example: What z-score corresponds to .975 area to the left? DISTR #3: invNorm (.975) ENTER = 1.96 CH. 3 Linear Regression & Correlation: Turn Diagnostic On in the CATALOG ENTER ENTER (yours should be on) Enter explanatory variable in L1. Enter response variable in L2. STAT CALC #8: LinReg (a+bx) L1, L2, Y1 [Y1 is in VARS Y-VARS #1: Function #1: Y1] STAT PLOT scatterplot Xlist: L1; Ylist: L2 ZOOM 9 will show the scatterplot with the LSRL drawn in. Residuals Explanatory in L1; Response in L2 L3 arrow up so L3 is highlighted L2 Y1(L1) ENTER Residual Plot Y= deselect the LSRL in Y1 define Y2 = 0 STAT PLOT scatterplot Xlist: L1; Ylist: L3 ZOOM 9

CH. 4 Transforming Exponential Data: Enter explanatory variable in L1. Enter response variable in L2. L3 arrow up so L3 is highlighted log(L2) ENTER You are now working with Lists 1 & 3 for all computations. Proceed like Ch. 3 with these adjustments. Transforming Power Data: Enter explanatory variable in L1. Enter response variable in L2. L3 arrow up so L3 is highlighted log(L1) ENTER L4 arrow up so L4 is highlighted log(L2) ENTER You are now working with Lists 3 & 4 for all computations. Proceed like Ch. 3 with these adjustments. CH. 5 Simulation: Assign #s to match scenario; MATH PRB #5: randInt ( enter interval & specify # of picks ENTER Example: Choose 5 people at random from a group of which 50% are employed, 20% are unemployed, and 30% are not in the labor force. Assign 0, 1, 2, 3, 4 = employed; 5,6 = unemployed; 7, 8, 9 = not in labor force. MATH PRB #5: randInt (0,9,5) ENTER CH. 7 Simulation of Random Digits Chosen in a Normal Distribution: MATH PRB #6: randNorm ( , , # of digits desired) ENTER Example: Womens heights are approximately normally distributed with N(64.5, 2.5) inches. Generate 200 random heights of women: MATH PRB #5: randNorm (64.5, 2.5, 200) STO L1 will put the numbers in a list so theyre more manageable. CH. 8

Binomial Coefficient for Formula -

n k

:

MATH PRB #3: nCr Note: you must enter n (# of trials) in the calculator before the above command.

Example: Calculate

5 3

, the number of ways you can have 3 successes with 5 options to choose from:

5 nCr 3 ENTER = 10

Binomial P.D.F.: DISTR (2nd VARS) A: binompdf (n, p, X) ENTER n = # of trials; p = probability of a success; X = exact number of success desired Calculates the probability in a binomial situation of exactly X number of successes. Binomial C.D.F.: DISTR (2nd VARS) B: binomcdf (n, p, X) ENTER n = # of trials; p = probability of a success; X = counts successes up to this number Calculates the probability in a binomial situation of up to X number of successes. Binomial Histogram: Enter # of successes sequentially in L1: L1 0, 1, 2, .... Enter probability of successes in L2: L2 arrow up so L2 is highlighted binompdf (n, p, L1) ENTER Scan L2 to set the appropriate WINDOW (ZOOM 9 does NOT work) Clear any equations in Y= STAT PLOT Histogram Xlist: L1; Freq: L2 Set WINDOW: X [-0.5, ....], Y [ 0, ....} TRACE, as sometimes bars are too small to see. One Simulation of a Binomial Probability Distribution: Define failure & success (usually 0 = failure, 1 = success) MATH PRB #7: randBin (defined success, p, n) where p = probability of success; n = # of trials Example: Simulate a person who is a 75% free throw shooter taking 12 shots: MATH PRB #7: randBin (1, 0.75, 12) ENTER Multiple Simulations of a Binomial Probability Distribution: MATH PRB #7: randBin (success, p, n) STO L1 : sum(L1) STO L2 (1) ENTER [sum is in LIST (2nd STAT MATH #5: sum] This simulates n trials & displays the number of successes in the simulation every time you press ENTER Geometric P.D.F.: DISTR (2 VARS) E: geometpdf (p, n) p = probability of success; n = number of trial where first success occursnd

CH. 10 Confidence Interval ( Known) with Raw Data Enter data in L1. STAT TESTS #7: ZInterval Data ENTER Enter in , L1, Freq: 1, and your desired confidence level Calculate ENTER Confidence Interval ( Known) with Statistics:

STAT TESTS #7: ZInterval Stats ENTER Enter in , ENTER

x , n, and your desired confidence level Calculate

One-Sample Z-Test ( Known): If you have raw data, enter in L1; if not skip to the next step & choose appropriately. STAT TESTS #1: Z-Test Data or Stats Enter Ho value, and appropriate values choose appropriate Ha Calculate ENTER CH. 11 Confidence Interval; Unknown: If you have raw data, enter in L1; if not skip to the next step & choose appropriately. STAT TESTS #8: TInterval Data or Stats Enter appropriate values and desired confidence level Calculate ENTER One-Sample T-Test ( Unknown): If you have raw data, enter in L1; if not skip to the next step & choose appropriately. STAT TESTS #2: T-Test Data or Stats Enter Ho value, and appropriate values choose appropriate Ha Calculate ENTER Two-Sample Confidence Interval; 1 & 2 Known: If you have raw data, enter in L1; if not skip to the next step & choose appropriately. STAT TESTS #9: 2-SampZInt Data or Stats Enter appropriate values and desired confidence level Calculate ENTER Two-Sample Confidence Interval; 1 & 2 Unknown: If you have raw data, enter in L1; if not skip to the next step & choose appropriately. STAT TESTS #0: 2-SampTInt Data or Stats Enter appropriate values and desired confidence level Pooled: No Calculate ENTER Two-Sample Z-Test ( 1 & 2 Known): If you have raw data, enter in L1; if not skip to the next step & choose appropriately. STAT TESTS #3: 2-SampZTest Data or Stats Enter Ho value, and appropriate values choose appropriate Ha Calculate ENTER Two-Sample T-Test ( 1 & 2 Unknown): If you have raw data, enter in L1; if not skip to the next step & choose appropriately. STAT TESTS #4: 2-SampTTest Data or Stats Enter Ho value, and appropriate values choose appropriate Ha Pooled: No Calculate ENTER CH. 12 One-Proportion Confidence Interval: STAT TESTS A: 1-PropZInt x: # of observations, n = sample size choose desired confidence level Calculate ENTER One-Proportion Z-Test: STAT TESTS #5: 1-PropZTest Enter Ho value x: # of observations, n = sample size choose appropriate Ha Calculate ENTER

Two-Proportion Confidence Interval: STAT TESTS B: 2-PropZInt x: # of observations, n = sample size choose desired confidence level Calculate ENTER Two-Proportion Z-Test: STAT TESTS #6: 2-PropZTest x: # of observations, n = sample size choose appropriate Ha Calculate ENTER CH. 13 Chi-Square Test for Goodness of Fit TI-83: Enter Observed in L1 & Expected in L2. To calculate 2 test statistic (O-E)2/E: L3 arrow up so L3 is highlighted (L1- L2)2/ L2 ENTER. We need the sum of the values in L3, so in the HOME SCREEN LIST (2nd STAT) MATH #5: sum (L3) ENTER. P-Value for 2 Goodness of Fit TI-83: 2nd VARS (DISTR) DRAW #3 Shade 2( (calculated value, upper boundary, d.f.) ENTER To clear drawings: 2nd PRGM (DRAW) #1 ClrDraw ENTER Chi-Square Test for Goodness of Fit & P-Value TI-84: Enter Observed in L1 & Expected in L2. STAT TESTS D: 2 GOF-Test Enter appropriate values [d.f.= n-1 where n is # of categories] Calculate Chi-Square Test for Independence or Homogeneity: Matrix A: Observed Matrix B: Expected STAT TESTS C: 2-Test... CALCULATE Shows the 2 calculated value, p-value, and degrees of freedom.

CH. 14 Inference for Regression Lines: Enter x (explanatory variable) in L1 and y (response variable) in L2. STAT TESTS [E or F} LinRegTTest Choose appropriate Ha Leave RegEQ blank ENTER