Question

To examine the differences between salaries of male and female middle managers of a large bank, 90 individuals were randomly selected, and two models were created with the following variables considered Salary- the monthly salary (excluding fringe benefits and bonuses), Educ the number of years of education, Exper the number of months of experience, Train the number of weeks of training, Gender- the gender of an individual; 1 for males, and O for females. Excel partial outputs corresponding to these models are available and shown below Model A: Salary: β0 + β|Educ + β2Exper + β3Train + β4Gender + ε Regression Statistics Multiple R R2 Adjusted R2 Standard Error Observations 0.8188 0.6704 0.6549 402.7513 90 df MS Regression Residual Total 28,029,969.88 13,803,720.12 41,833,690.00 StandardError 9,343,323.29 58.21 86 89 162,508.37 Coefficients t-stat 14.40 7.07 7.20 6.41 p-value 1.21E-24 3.78E-10 2.10E-10 7.39E-09 4,713.26 139.54 3.35 609.25 327.1967 19.7253 0.4649 94.9920 Intercept Educ Exper Gender The variable Train is deleted from Model A which results in Model B. Which of the following justifies this choice? R2 of Model B is lower than the adjusted R2 of Model A, and the variable is not individually significant in Model A. The adjusted The adjusted R2 of Model B is higher than the adjusted R2 of Model A, and the variable is individually significant in Model A. The adjusted R2 of Model B is higher than the adjusted R2 of Model A, and the variable is not individually significant in Model A. The adjusted R2 Model B is lower than the adjusted R2 of Model A, and the variable is individually significant in Model A

Answer 1

Answer

Adjusted R^2 in model A = 0.6549

Adjuested R^2 in model B = 0.6585

and individual p value of train variable in model A is 0.7543

it is clear that adj R^2 for model B is higher than model A and the p value for train variable on individual basis is greater than 0.05 significance level, which means that the train variable is not significant

therefore, option C