Multiple Regression Example - Statistics Department
Multiple Regression Example
The dean of an MBA program wants to base admissions on who is most likely to succeed in the program. She regards a student’s MBA program GPA as a measure of their success. She believes the primary determinants of success are the following:
Undergraduate grade point average (GPA)
Graduate Management Admissions Test Score (GMAT) score
Number of years of work experience
She randomly samples students who completed the MBA and recorded their MBA program GPA, as well as the three variables listed above. These are stored in the file mba.jmp for Chapter 19.
To fit the multiple regression model, we click on Analyze, Fit Model; put MBA GPA in Y, Response; click on UnderGPA, GMAT and Work and click Add (these three variables should now appear in the Construct Model Box) and then click Run Model.
Response MBA GPA
Whole Model
Actual by Predicted Plot
[pic]
Summary of Fit
|RSquare |0.463532 |
|RSquare Adj |0.444597 |
|Root Mean Square Error |0.787938 |
|Mean of Response |8.156517 |
|Observations (or Sum Wgts) |89 |
Analysis of Variance
|Source |DF |Sum of Squares |Mean Square |F Ratio |
|Model |3 |45.597249 |15.1991 |24.4812 |
|Error |85 |52.771971 |0.6208 |Prob > F |
|C. Total |88 |98.369220 | ||t| |
|Intercept | |0.4660931 |1.505631 |0.31 |0.7576 |
|UnderGPA | |0.062827 |0.11993 |0.52 |0.6017 |
|GMAT | |0.0112814 |0.001383 |8.16 | F | |
|UnderGPA |1 |1 |0.170380 |0.2744 |0.6017 | |
|GMAT |1 |1 |41.327192 |66.5659 | ................
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