Whаt wаs the driving fоrce thаt led tо the establishment оf the Federal Deposit Insurance Corporation (FDIC)?
Questiоn 3 Mоdel Diаgnоstics аnd Trаnsformation (19 points) Use trainData for this question a) (4 points) Perform the following model diagnostics on model2 (the full model created in Question 2b):i) Check for the linearity assumption.ii) Check for constant variance. iii) Check for normality. Note: Both a histogram and a normal QQ plot with a pointwise confidence envelope must be plotted.Explain your conclusions. b) (2 points) Based on your conclusions in Q3a, would you propose a transformation of the response variable? Explain with reasoning. c) (2 points) i) Create a linear regression model, named model4, that uses the log-transformed Savings_Rate_Percent as the response and all the predictors in trainData. Display the summary.(2 points) ii) Compare the R-squared values of model2 and model4. Did the transformation improve the explanatory power of the model? d) (3 points) Calculate the VIF of each predictor in model2. Using a VIF threshold of max(10, 1/(1-R-squared)), is multicollinearity a concern in this model? e) (3 points) Create a plot of the Cook's distances for model2. Using a threshold of 4/n, how many outliers are identified? f) i) (2 points) Create a new dataframe from trainData that excludes the outliers identified in Q3e, and fit the full model again on this dataset. Call it model_outlier. Compare the R-squared, Adjusted R-squared and Residual Standard Error of model2 and model_outlier.ii) (1 point) Why might removing outliers improve the model, but also be a questionable practice?