Use R studio to solve this problem

From Assignment 2 Problem 4(c), variables CGDUR, MEM and SOCIALSU are consistently selected as the best predictors.

KBI=read.csv("https://raw.githubusercontent.com/DanikaLipman/DATA603/main/KBI.csv", header = TRUE) model=lm(BURDEN~(CGDUR+ MEM +SOCIALSU) , data=KBI) summary(model)

interactionmodel=lm(BURDEN~(CGDUR+ MEM +SOCIALSU)^2 , data=KBI) summary(interactionmodel) 

From the output above, none of interaction terms are significant. Therefore,the final model for prediction is BURDEN = 115.539 + 0.566MEM 0.4923750CTALSU + 0.121CGDUR Use the final model above to answer the following questions a. Check normality, homoscedasticity, and linearity assumptions. b. Do you detect any outliers by using leverage values greater that Po If yes, create a new data set that removes these outliers. c. Fit the model BURDEN = fy + 8, MEM + B.SOCIALSU + B3CGDUR again using the new dataset created in part (b). Compare the results with the model the final model from From Assignment 2 Problem 4(c) . Do you notice any difference in the results of this model using two different data sets? Compare RSE, significance, and adjusted R