Question

- + Fit to page Page view Aº read al 06. Jeff Sagarin has been providing sports ratings for USA Today since 1985. In baseball, his predicted RPG (runs per game) statistics considers the entire player's offensive statistics and is claimed to be the best measure of a player's true offensive value. A set of sample data was collected for RPG and a variety of offensive statistics for a recent Major League Baseball (MLB) season for members of the New York Yankees. The variables are defined as follows: RPG, predicted runs per game statistics, H, hits, 2B, doubles; 3B, triples, HR, home runs; RBI, runs batted in: BB, bases on balls (walks); SO, strikeouts: SB, stolen bases; CS, caught stealing, OBP, on-base percentage; SLG, slugging percentage, and AVG, batting average Let the dependent variable be the RPG statistics for the regression analysis. Based on the above information about RPG, the following output is taken from applying Backward Elimination procedure to fit a regression model (consider a to remove -0.05). Regression Analysis: RPG versus H. 2B, 3B, HR, SO, OBP, AVG Candidate terms: H, 2B, 3B, HR, SO, OBP, AVG Constant H 2B --Step 1 ----- ------Step 2... Coef P Coef P -0.424 -0.411 -0.00553 0.458 -0.00508 0.382 0.0021 0.918 0.388 0.018 0.386 0.014 0.1094 0.000 0.1090 0.0590 -0.01003 0.088 -0.00001 0.476 28.25 0.000 28.21 0.000 -18.13 0.001 -18.14 0.000 3B HR SO OBP AVG S 0.443326 0.426130 97.34% 9733% 95.78% 96.10% R-sq R-sq(adj) R-sq(pred) Mallows' Cp 91.1896 93.15% 6.01 8.00 (3pt) Which variable will be dropped from the model in Step 2? For full credit, explain as to how you come with the answer.

Answer 1

we first consider all the cant ones. (foo zemoval) independent variables variability In hackward elimination, independent variables in the model and then diep remove the not significant Here Model- RPG -0.424 -0.00553 H +0.021126) +0.383 (36) + 0.1094 (HR) -0.01003 (SO) +26.25 (OBP) – 18.13 CANVG) Now, here we have x=0.05 we considered all the explaining RPG and R² = 91.37% meaning 9 97.34% in RPG is explanned by these Now, we dsop all these variables that have p-value greater than 0.05 Because, for each variable , Hot ßi = 0 reject Ho when p-value and if pvalue > 0.05, fail to syect Ho A fize insignificant variables to be dropped) semoved olin step 2) 0 So as it has "p-value = 0.476 > 0.05 and has maxinum it most insignificant p-value among lates, H with pvalue = 0.382 >0.05 (might change in step 3) will be removed Note : 2B is alseady s semoved in Step I with qualue (maximum =O VA vs Hißito 0 and we <0.05 we or all 2 0.918 > 0.05

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