Run the following linear regression was run to predict Wins…
Run the following linear regression was run to predict Wins in the MLB from 2015-2023 (minus 2020) based on the following statistics: OPS: On-base percentage + Slugging percentage WHIP: Walks + hits given up per inning pitched (You can copy this code directly into your R session–and should have done so prior to the quiz) teams15_23 %filter(yearID >= 2015, yearID !=2020) %>%mutate(OBP = (H + BB + HBP)/(AB + BB + HBP + SF), SLG = (H + X2B + 2 * X3B + 3 * HR)/AB,OPS = OBP + SLG,WHIP = (BBA + HA)/(IPouts/3)) The standard deviations of OPS and WHIP are 0.037 and 0.096 respectively. If I could take an average team in OPS and WHIP to the 84th percentile (one standard deviation above or below average—since low WHIP is better) in one (and only one) of the two statistics, which would I prefer? (i.e., would I get more wins by increasing OPS by one standard deviation or decreasing WHIP by one standard deviation?)—Check Mathletics Ch. 18 (This isn’t as hard as you may think!) First, how many additional wins would I expect to get if I increased my team OPS by 1 standard deviation? Report your answer to 2 decimal places.