Moneyball tells the story of the 2002 Oakland Athletics, a Major League Baseball (MLB) team, and how they found ways to improve despite being one of the poorest teams in baseball. To do this, Billy Beane (the general manager) and Paul DePodesta (a Harvard graduate) analyzed available data on what made teams successful. After their initial analysis, they determined that scoring runs was very important to a team's success. In this problem you will explore what variables can be used to predict how many runs a team will score in a season. The data set you will use contains information on all MLB teams between 1999 and 2012 (420 total observations). The variables available include Variable Description Team League Year RS RA OBP SLG BA Abbreviated team name What league the team belongs to (0 = AL, 1 = NL) Year Runs scored Runs allowed On-base percentage Slugging percentage Batting average This question focuses on model 2. Below is the output from statistical software. t value Estimate -885.8 Std. Error 26.658 -33.23 (Intercept) League OBP 2.241 -15.54 28.78 1.239 -6.93 23.23 24.72 SLG 16.61 0.672 Consider two teams with identical on-base and slugging percentages, but one is in the National League (NL) while the other is in the American League (AL). How many more/fewer runs is the NL team expected to score than the AL team? O A. The NL team is expected to score 885.8 fewer runs than the AL team. O B. The NL team is expected to score 2.241 more runs than the AL team. O C. The NL team is expected to score 15.54 fewer runs than the AL team. O D. The NL team is expected to score 26.66 more runs than the AL team. Moneyball tells the story of the 2002 Oakland Athletics, a Major League Baseball (MLB) team, and how they found ways to improve despite being one of the poorest teams in baseball. To do this, Billy Beane (the general manager) and Paul DePodesta (a Harvard graduate) analyzed available data on what made teams successful. After their initial analysis, they determined that scoring runs was very important to a team's success. In this problem you will explore what variables can be used to predict how many runs a team will score in a season. The data set you will use contains information on all MLB teams between 1999 and 2012 (420 total observations). The variables available include Variable Description Team League Year RS RA OBP SLG BA Abbreviated team name What league the team belongs to (0 = AL, 1 = NL) Year Runs scored Runs allowed On-base percentage Slugging percentage Batting average This question focuses on model 2. Below is the output from statistical software. t value Estimate -885.8 Std. Error 26.658 -33.23 (Intercept) League OBP 2.241 -15.54 28.78 1.239 -6.93 23.23 24.72 SLG 16.61 0.672 Consider two teams with identical on-base and slugging percentages, but one is in the National League (NL) while the other is in the American League (AL). How many more/fewer runs is the NL team expected to score than the AL team? O A. The NL team is expected to score 885.8 fewer runs than the AL team. O B. The NL team is expected to score 2.241 more runs than the AL team. O C. The NL team is expected to score 15.54 fewer runs than the AL team. O D. The NL team is expected to score 26.66 more runs than the AL team