Question is attached

A business statistics professor at State College would like to develop a regression model to predict the final exam scores for students based on their current GPAs, the number of hours they studied for the exam, the number of times they were absent during the semester, and their genders. Use the accompanying data to complete parts a through e. Click the icon to view the data. a. Use advanced statistical software or an Excel Add-in like PHStat to check for the presence of multicollinearity. Find the variance inflation factor (VIF) for each independent variable. Use x, = GPA, X2 = Hours, X3 = Absences, and x4 = Gender, where x, = 1 if the gender is male, and x =0 otherwise. (Round to one decimal place as needed.) Independent Variable VIF GPA (X1. Hours ( X2 ) Absences (X3) Gender (X4) Use x, = GPA, X2 = Hours, X2 = Absences, and x, = Gender, where x, = 1 if the gender is male, and x, = 0 otherwise. Note that the coefficient is 0 for any variable that was removed or is not significant. y =[Score 68 69 70 71 74 77 77 78 78 79 79 82 83 84 84 84 84 85 85 85 86 86 86 87 87 89 89 90 90 91 91 92 92 92 93 94 98 99 100 100 GPA 2.48 2.24 2.61 3.11 3.08 2.72 3.31 2.98 3.05 2.78 2.8 3.42 3.22 3.12 3.12 2.95 2.77 3.2 3.8 3.57 2.92 3.04 3.16 3.89 3.49 3.33 3.6 2.87 3.4 3.22 3.79 3.96 4 3.65 2.98 3.26 2.85 3.7 3.55 3.06 Hours 3 4 2.5 0.5 6 3.5 1.5 3 2 2.5 4.5 7 3 3 5.5 2 4 4.5 2 3.5 6 6.5 5 7.5 4 6.5 5 3.5 6 4.5 7 6 5 6.5 4 6.5 3.5 5 6.5 7 Absences Gender 0 0 3 0 1 0 0 1 4 1 6 0 0 0 3 1 3 1 2 1 0 1 1 0 1 0 4 0 0 1 0 0 1 1 3 0 0 1 2 0 1 1 1 0 3 1 4 0 0 1 1 1 0 1 1 0 1 0 2 0 0 0 2 1 0 0 1 0 2 1 0 1 0 0 1 0 1 1 0 0