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First, add a variable to change the type of bus (diesel or gasoline) to a qualitative variable. If the bus type is diesel, then set the qualitative variable to 0. If the bus type is gasoline, then set the qualitative variable to 1. Develop a regression equation using statistical software with maintenance as the dependent variable and age, miles, and bus type as the independent variables. a. Write out the multiple regression equation analysis. Discuss each of the variables. b. Determine the value of R2. Interpret. c. Develop a correlation matrix. Which independent variables have strong or weak correlations with the dependent variable? Do you see any problems with multicollinearity? d. Conduct the global test on the set of independent variables. Interpret. e. Conduct a test of hypothesis on each of the independent variables. Would you consider deleting any of the variables? If so, which ones? f. Rerun the analysis until only significant regression coefficients remain in the analysis. Identify these variables. g. Develop a histogram or a stem-and-leaf display of the residuals from the final regression equation developed in part (f). Is it reasonable to conclude that the normality assumption has been met? h. Plot the residuals against the fitted values from the final regression equation developed in part (f) against the fitted values of Y. Plot the residuals on the vertical axis and the fit-ted values on the horizontal axis.
Age
Miles
Type
X3
X4
X5
7
853
Diesel
10
883
822
865
Gasoline
751
8
870
5
780
9
857
3
818
869
848
845
885
12
838
760
741
859
826
806
858
2
785
828
11
980
803
6
819
821
798
815
757
1008
831
849
839
812
809
4
864
799
844
832
842
792
775
882
874
837
774
1
823
790
800
827
830
14
895
804
866
851
835
846
802
847
856
817
784
816
836
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