Predictors with α = 0.05Which predictors do you consider significant?Which predictors would you be comfortable eliminating? SUMMARY OUTPUTRegression StatisticsMultiple R0.9761368R Square0.9528431Adjusted R Sc 0.9486514Standard Erro 84.864073Observations50ANOVAdfSSMSSignificance FRegression465484221637105.5227.315433.205E-29Residual45324085.997201.9109Total496872508Coefficients tandard Erroit StatP-valueLower 95%Upper 95% Lower 95.0% Upper 95.0%Intercept19.52036856.4596190.34574030.731149 -94.195142133.23588 -94.195142133.23588Floor Area (Se0.2599690.0182476 14.2467382.74E-180.22321640.2967216 0.2232164 0.2967216Offices74.27224726.0094252.85558970.00647721.886575126.6579221.886575126.65792Entrances49.3180729.0074881.70018410.095999 -9.1060107107.74215 -9.1060107107.74215Age0.94232870.97665290.96485530.3397741 -1.02475122.9094087 -1.02475122.9094087Е.What is the R2?What is the adjusted R2?Explain the purpose of having an adjusted R2?

Instructions : "Predictors with α = 0.05 Which predictors do you consider significant? Which predictors would you be comfortable eliminating? " Given : Significance level , α = 0.05 For the significance of predictors, we always look for their respective P-values . If they have the P-values less than the significance level, then we consider those predictors as significantly contributing to the regression model. From the third table , we have the P-values of all the predictors. We noticed that : P-value of Floor Area = 0 which is less than significance level= 0.05 . Hence , Floor Area is significant. P-value of Offices = 0.0064 which is less than significance level= 0.05 . Hence , Offices is significant. P-value of Entrances = 0.096 which is greater than significance level= 0.05 . Hence , Entrances is not significant. P-value of Age = 0.3398 which is greater than significance level= 0.05 . Hence , Age is not significant. Predictors which are not significant , we can eliminate them. Hence, we will eliminate Entrances and Age. Here R-square = 0.952 which means the regression model is explaining 95.2% of the variability in dependent variable. Adjusted R-square = 0.9486. Adjusted R-square is the value similar to R-square which has been adjusted for the number of predictors in the model we have.