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Introduction to linear regression Batter up The movie Moneyball focuses on the “quest for the secret of success in baseball”. It follows a low-budget team, the Oakland Athletics, who believed that underused statistics, such as a player’s ability to get on base, betterpredict the ability to score runs than typical statistics like home runs, RBIs (runs batted in), and batting average. Obtaining players who excelled in these underused statistics turned out to be much more affordable for the team. In this lab we’ll be looking at data from all 30 Major League Baseball teams and examining the linear relationship between runs scored in a season and a number of other player statistics. Our aim will be to summarize these relationships both graphically and numerically in order to find which variable, if any, helps us best predict a team’s runs scored in a season. The data Let’s load up the data for the 2011 season. download.file("", destfile = "mlb11.RData") load("mlb11.RData") In addition to runs scored, there are seven traditionally used variables in the data set: at-bats, hits, home runs, batting average, strikeouts, stolen bases, and wins. There are also three newer variables: on-base percentage, slugging percentage, and on-base plus slugging. For the first portion of the analysis we’ll consider the seven traditional variables. At the end of the lab, you’ll work with the newer variables on your own. 1.What type of plot would you use to display the relationship between runsand one of the other numerical variables? Plot this relationship using the variable at_batsas the predictor. Does the relationship look linear? If you knew a team’s at_bats, would you be comfortable using a linearmodel to predict the number of runs?
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