Chapter 6 Text Example
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SPSS Procedures for Chapter Examples
Chapter 6 Linear Regression: Prediction
In this module, we will learn to run a simple linear regression analysis with SPSS, obtaining the
coefficients to generate the regression equation. We will use the same set of data – hsb500R to
explore whether “mathach” (Math achievement scores) can be predicted from “visual” (Visualization
test scores); and from “mosaic” (Detecting patterns test scores).
Step 1. Getting start
a. Click on SPSS icon to launch the program.
b. Open the data file “hsb500R”. Make sure the window is in the Data View mode.
Step 2. Run SPSS regression analysis (mathach – visual):
a. From Analyze menu choose Regression, and then Linear.
b. You will see Linear Regression dialogue box. From the variable list (in the right box), click on
“mathach” (Math achievement), and then Click on the arrow button under Dependent, this
defines the “mathach” variable as dependent variable (or response variable, criterion variable).
c. Click on “visual” (Visualization test), then click on the arrow button under Independent(s); this
defines Visual variable as the independent (predictor variable).
(In this exercise, we only need to obtain the coefficients to generate regression equation . more
functions of the procedure will be introduced in Chapter 17 exercises.)
By Leping Liu
Chapter 6 Text Example
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d. Click on OK, see the output:
e. Save the output as “reg6out1” and print it out.
f. Close the output window, and back to the data editor window ready to do the second run:
Step 3. Run SPSS regression analysis (mathach – mosaic):
a. From Analyze menu choose Regression, and then Linear.
b. Repeat Step 2 – b and c. Define “mathach” (Math achievement) as dependent, and “mosaic”
(Detecting patterns test )variable as independent (predictor variable).
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Chapter 6 Text Example
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c. Click on OK, see the output:
d. Save it as “reg6out2” and print it out.
e. Close the output window.
Now you can continue the next step on the Web and use the output results to generate the regression
equations.
By Leping Liu