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SPSS WORKSHEET 6: (MULTIPLE REGRESSION)

SPSS WORKSHEET 6: (MULTIPLE REGRESSION)

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SPSS Worksheet 6: (Multiple Regression)
 
Instructions: Lesson 34 Exercise File 1 is located at the end of the chapter under the heading Exercises in your Green and Salkind textbook. Complete the exercise and then complete the worksheet below by filling in the blanks and answering the questions.
 
H01: There will be no significant predictive relationship between the criterion variable (Stats Exam Scores) and the linear combination of predictor variables (Math test, English test, English GPA, Math GPA, and Other GPA) for college students.
Assumptions
 
Assumption of Bivariate Outliers: Hint: Run scatter plots between each pair of predictor variables (x, x) and also the predictor variables (x) and the criterion variable (y). You will need a total of 15 individual plots. Look for “extreme” bivariate outliers. See Warner pp. 165-166 and 169.
 
Assumption of Multivariate Normal Distribution: Hint: Run scatter plots between each pair of predictor variables (x, x) and also the predictor variables (x) and the criterion variable (y). Look for the classic “cigar shape.”  You will need a total of 15 individual plots. See Warner p. 269.
 
Insert Graph or Table Here
 
Fill in the blanks:
Variables

Is the assumption of bivariate outliers tenable?

Is the assumption of multivariate normal distribution tenable?

Math test (x)   Stats Exam (y)

English test (x)   Stats Exam (y)

English GPA (x)   Stats Exam (y)

Math GPA (x)    Stats Exam (y)

Other GPA (x)    Stats Exam (y)

English test (x)   Math test (x)

English GPA (x)   Math test (x)

Math GPA (x)    Math test (x)

Other GPA (x)    Math test (x)

English GPA (x)   English test (x)

Math GPA (x)    English test (x)

Other GPA (x)    English test (x)

Math GPA (x)    English GPA

Other GPA (x)    English GPA

Other GPA (x)    Math GPA (x) 

 
Assumption of non-Multicollinearity among the Predictor Variables: If a predictor variable (x) is highly correlated with another predictor variable (x), they essentially provide the same information about the criterion variable. If the Variance Inflation Factor (VIF) is too high (greater than 10), you have multicollinearity and have violated the assumption. Acceptable values are between 1and 5. To run the VIF test, select Analyze > Regression > Linear > Statistics > then check the Collinearity diagnostics checkbox.
 
Insert Graph or Table Here
Fill in the blanks:
Variables

VIF

Is the assumption of non-Multicollinearity met?

Math test

 
 

English test

 
 

English GPA

 
 

Math GPA

 
 

Other GPA

 
 

 
Results
Insert the ANOVA a table below:
 
Insert Graph or Table Here
Fill in the blanks:
Regression Model
Regression Model (ANOVA a)
 

Value

d.f. between Groups

 
 

d.f. within Groups

 
 

F-statistic

 
 

F-critical (See Appendix C in Warner)

 
 

p- value

 
 

 
Is the F- statistic greater than F-critical?
Answer:
 

 
Is the p- value less than .05?
Answer:
 

 
Is the predictive model statistically significant?
Answer:
 


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