Salar Activities 40 46 38 39 37 38 42 37 GPA 3.2 3.6 2.8 2.4 2.5 2 SUMMARY OUTPU

Question

Salar Activities 40 46 38 39 37 38 42 37 GPA 3.2 3.6 2.8 2.4 2.5 2 SUMMARY OUTPUT Regression Statistics 4 5 Multiple R 6 R Square 7 Adiusted R Square 8 Standard Error 9 Observations 10 11 ANOVA 12 13 Regression 4 Residual 15 Total 0.9080 0.8245 0.7743 1.4479 4 2.7 2.6 3.0 2.9 3 2 4. df Significance F 0.0023 MS 68.9244 14.6756 83.6000 16.4379 34.4622 2.0965 17 18 Intercept 19 GPA 20 Activities Coefficients Standard Error 3.1919 1.2342 0.5291 t Stat P-value Lower 95% Upper 95% 24.3092 3.8416 1.6810 7.6159 3.1127 3.1768 0.0001 0.0170 0.0156 16.7615 0.9233 0.4298 31.8569 6.7600 2.9322

Explanation / Answer

Answer to the question is as follows:

a. MLR equation is :

Est. Start Salary = 24.3092 + 3.8416*GPA + 1.6810*Activities

b. Per unit increease in GPA, Est. start Salary increases by 3.8416 units
Per unit increase in Activities , Est. Start Salary increases by 1.6810

c.BOth the variables, GPA and Activities have p-values less than .05 i.e. .0170 and .0156
Since a variable having lower than critical p-value is statistically significant, both variables GPA and Activities are statistically significant

d. Model significance can is a F-test/ANOVA test which in our case has gotten a p-value of .0023, and hence is equation is statistically significant

e. Rsquare or the coefficient of determination is .8245 from the 1st table. It means that 82.45% of variation in Est. Start salary is being explained by GPA and Activities. This is a pretty decent Rsquare, therefore showing that the preditive power of this equation is decently strong

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