Need help with homework problems #2 and #3! Please answer thoroughly in complete sentences! Thank you!

Question 2 Recall the Akaike information Criterion (AlC) as well as the related Bayesian Information Criterion (BIC) from our study of Variable Not yet Selection and Model Building. Without heavy mathematical notation, explain how both the AIC and BIC emphasize goodness-of-fit of answered a regression model while also controlling model complexity. Why would we wish to do this (that is, to promote goodness-of-it while at Points out of the same time controlling model complexity)? Why is model complexity so undesirable? 1.00 Flag question 1 A B I Ff Question 3 Does Stepwise Regression using, say, the AIC criterion necessarily always find an optimal choice of predictor variables -- optimal Not yet (lowest) according to the AIC - among a given set of candidate predictor variables? Why or why not? answered Points out of 1.00 1 A v B I Ff E GO SS Flag