3. Use the following MS Excel multiple regression output (response variable = COLL_GPA) to complete/ answer following questions. (Problems a) through f): 1 point; g): 2 points) SUMMARY OUTPUT - COLL_GPA = f(FEMALE, SAT, HS_GPA, HRS_STUDY, HRS_CELL) Regression Statistics Multiple R 0.4317 R Square 0. 1863 Adjusted R Square 0. 1606 Standard Error 0.3035 Observations 164 ANOVA of SS MS F Significance F Regression 5 3.3319 0.6664 7.2360 3.9738E-06 Residual/Error 158 14.5507 0.0921 Total 163 17.8826 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 1.4746 0.5336 2.7635 0.0064 0.4207 2.5284 FEMALE 0.1264 0.0492 2.5705 0.0111 0.0293 0.2234 SAT 0.0006 0.0003 1.8996 0.0593 -0.000022 0.0011 HS_GPA 0.4267 0.1344 3.1738 0.0018 0.1611 0.6922 HRS_STUDY 0.0220 0.0126 -1.7519 0.0817 0.0468 0.0028 HRS_CELL -0.0213 0.0070 -3.0411 0.0028 -0.0352 -0.0075 COLL_GPA Student's college GPA FEMALE Indicator variable: FEMALE=1, MALE=0 SAT Student's SAT score HS_GPA Student's high school GPA HRS_STUDY Average number of hours a student studies per day HRS_CELL Average number of hours a student uses a cellphone per day a) What percent of total variation in COLL_GPA does the model overall explain/ determine? b) Does the model overall fit the data? NO YES (please circle one) c) Should one interpret the estimated value for the intercept? NO YES (please circle one) d) Interpret the value for the estimated coefficient for HRS_CELL: Parts e) and f) are interpreted, "controlling for the effects of all other explanatory variables&quot