Data from Example 5 (page 723) are used to illustrate the steps.
TI-83/84
Step 1 Enter the X (Time) data in L1 and the Y (Score) data in L2.
Step 2 Press STAT, highlight CALC, and press 4 to choose LinReg(ax+b). On the home screen, the following command appears: LinReg(ax+b).
Step 3 Press ENTER. The output shows . The TI-83/84 denotes the slope β1 as a and the y intercept b0 as b. Thus, the TI-83/84 is telling you that the estimated regression equation is .
Step 4 Now Press STAT again and highlight TESTS.
Step 5 Press the down arrow key until LinRegTTest is highlighted.
Step 6 Press ENTER. The LinRegTTest menu appears.
Step 7 For Xlist, enter L1 (or whichever list you entered the X data in).
Step 8 For Ylist, enter L2 (or whichever list you entered the Y data in).
Step 9 For Freq, enter 1, and for β & p highlight “≠ O” and press ENTER.
Step 10 Move the cursor over Calculate, make sure all your entries are correct, and press ENTER. The results are as shown in Figure 10 (page 724).
EXCEL
Step 1 Enter the “Time” variable in column A and the “Score” variable in column B.
Step 2 Click on Data > Data Analysis > Regression and click OK.
Step 3 For Input Y Range, select cells B1–B10. For Input X Range, select cells A1–A10. Make sure Labels is unchecked.
Step 4 If you want to verify the regression assumptions, then select Residual Plots and Normal Probability Plots.
Step 5 Click OK. The results are as shown in Figure 11 (page 725).
MINITAB
Step 1 Enter the “Time” variable in C1 and the “Score” variable in C2.
Step 2 Click on Stat > Regression > Regression > Fit Regression Model.
Step 3 Move “Score” to the Responses box, and “Time” to the Continuous predictors box.
Step 4 If you want to verify the regression assumptions, click the button labeled Graphs… and select Four in One.
Step 5 Click OK twice. The results are as shown in Figure 12 (page 725).
SPSS
Step 1 Input the time and score data into the first two columns. Under the Variable View tab, rename the columns Time and Score.
Step 2 Select Analyze > Regression > Linear….
Step 3 Choose Score as the Dependent variable and Time as the Independent variable.
Step 4 If you want to verify the regression assumptions, click Plots… and select Normal probability plot. Click Continue.
Step 5 Click OK.
731
JMP
Step 1 Click File > New > Data Table. Input the time and score data into the first two columns. Rename the columns Time and Score.
Step 2 Select Analyze > Fit Y by X. Choose Score as the Y, Response and Time as the X, Factor. Click OK.
Step 3 Click the red triangle beside “Bivariate Fit of Score by Time,” and select Fit Line. The desired output is in the Parameter Estimates table.
Step 4 If you want to verify the regression assumptions, click the red triangle beside Linear Fit, and select Plot Residuals.
CRUNCHIT!
Step 1 Input the time and score data into the first two columns.
Step 2 Select Statistics > Regression > Simple Linear. Choose Var2 as the Dependent Variable and Var1 as the Independent Variable.
Step 3
If you want to verify the regression assumptions, select Residuals Plot as the Display and click Calculate.
To observe the test statistics and p-values, select Numeric Results as the Display and click Calculate. Results are shown in Figure 13 of Example 5.