Statistics 101A: Homework # 7 Fall 2016 Problems assigned from the book Problem Two from Chapter seven – page 255 Q3 from Chapter 7: An avid fan of the PGA tour with limited background in statistics has sought your help in answering one of the age-old questions in golf, namely, what is the relative importance of each different aspect of the game on average prize money in professional golf ? The following data on the top 196 tour players in 2006 can be found on ccle in the file pgatour2006.csv: Y , PrizeMoney = average prize money per tournament x 1 , Driving Accuracy is the percent of time a player is able to hit the fairway with his tee shot. x 2 , GIR, Greens in Regulation is the percent of time a player was able to hit the green in regulation. A green is considered hit in regulation if any part of the ball is touching the putting surface and the number of strokes taken is two or less than par. x 3 , Putting Average measures putting performance on those holes where the green is hit in regulation (GIR). By using greens hit in regulation the effects of chipping close and one putting are eliminated. x 4 , Birdie Conversion% is the percent of time a player makes birdie or better after hitting the green in regulation. x 5 , SandSaves% is the percent of time a player was able to get “up and down” once in a greenside sand bunker. x 6 , Scrambling% is the percent of time that a player misses the green in regulation, but still makes par or better. x 7 , PuttsPerRound is the average total number of putts per round.( http://www.pgatour.com/r/stats/; accessed March 13, 2007) The golf fan was so impressed with your answers to part 1 (HW6 Q5) that your advice has been sought re the next stage in the data analysis, namely using model selection to remove the redundancy in full the model developed in part 1. log(Y) = B0 + B1x1 + B2 x2 + B3x3 + B4 x4 + B5x5 + B6 x6 + B7 x7 + e (Model 7.10) where the description of the variables is as listed above.Interest centers on using variable selection to choose a subset of the predictors to model the transformed version of Y. Throughout this question we shall assume that model (7.10) is a valid model for the data. A. Identify the optimal model or models based on Radj , AIC, AICC, BIC from the approach based on all possible subsets. B. Identify the optimal model or models based on AIC and BIC from the approach based on backward selection. C. Identify the optimal model or models based on AIC and BIC from the approach based on forward selection. D. Carefully explain why the models chosen in (a) & (c) are not the same while those in (a) and (b) are the same. E. Recommend a final model. Give detailed reasons to support your choice. F. Interpret the regression coefficients in the final model. Is it necessary to be cautious about taking these results to