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4. A financial analyst wants to examine the relationship between salary (in $1 000) and four explanatory variables: age (X1 = Age), experience in the field (X2 = Exper.), number of degrees (X3 = Degrees), and number of previous jobs in the field (X4 = Prevjobs). He took a sample of 20 employees and obtained the following Excel output: SUMMARY OUTPUT Regression Statistics Multiple R 0.992 R Square 0.984 Adjusted R square 0.979 Standard Error 2.26743 Observations 20 ANOVA df SS MS F Significance F Regression 4 4609.83164 1152.45791 224.160 0.00001 Residual 15 77.11836 5.14122 Total 19 4686.95000 Coefficients Standard Error t-Stat P-value Intercept -9.611198 2.77988638 -3.457 0.0035 Age 1.327695 0.11491930 11.553 0.0001 Exper. 0.106705 0.14265559 -0.748 0.4660 Degrees 7.311332 0.80324187 9.102 0.0001 Prevjobs -0.54168 0.44771573 -1.126 0.2778 a) What is the regression equation (round all values to 2 d.p.)? [2] b) What is the predicted salary for a 35-year-old, with 10 years of experience, 3 degrees and 1 previous job? [1] c) What percentage of the variation in salary is explained by the four explanatory variables? [1] d) Based on the significance F-test, does this model give a good prediction? (Test at the 5% significance level and explain your conclusion) [2] e) Which of the explanatory variables might you want to remove from the regression to improve the fit of your model? [2]