Question 3: Stepwise Regression – 13 points For this questio…

Questiоn 3: Stepwise Regressiоn - 13 pоints For this question, use the unstаndаrdized dаta (trainData).             a. Perform forward stepwise regression using BIC. Let the minimum model be the one with only an intercept, and the  maximum model to be model1. Display the model summary of your final model. Call it forward_model. (2 points) NOTE: For R, keep the max iteration of 1000 when running the step function. NOTE: For Python, keep the max iteration of 1000 when running the stepwise function.   Which variables were selected in the forward_model? Which regression coefficients are significant at the 99% confidence level in forward_model? (1.5 points) At which BIC value did the model cease selecting predictors, and what was the reason for this? (1.5 points) b. Perform forward-backward stepwise regression model using AIC, starting with intercept-only model. Call it both_model. (1 point) NOTE: For R, keep the max iteration of 1000 when running the step function. NOTE: For Python, keep the max iteration of 100 when running the stepwise function.   i) Which variables are selected in both_model? (1 point) ii) Are all the selected variables significant at 99% level? Explain the reason. (1.5 points) iii) If any variable is not significant at 99% level, why might it still be included? (1.5 points) c. Perform 2 Partial F-tests to compare the both_model (3b) with the full model (model1) and the forward model with model1. What is your interpretation at the 95% confidence level? (2 points) d. What is the difference in variable selection between forward, backward and forward-backward stepwise regresssion? (1 point)