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Data Analysis in Major League Baseball Name Affiliation Course Instructor Due Date Data Analysis in Major League Baseball In this paper, multiple regression analysis was conducted to answer two hypotheses that included a dependent variable and two independent variables. A multiple regression analysis is a statistical approach that describes the relationship between an outcome variable and two or more independent predictor variable by detailing how changes in the independent variable influence the outcome variable (Uyanik & Guler, 2013). The dataset used in the exercise was the statistics for the 2010 season of Major League Baseball (MLB). The dataset has 12 variables and covers 11 statistics on 30 teams that were part of the 2010 MLB season. Hypothesis 1 The first hypothesis examined the relationship between the number of games won with earned run average and strikeouts. Earned run average (ERA) describes the mean number of earned runs allowed by a pitcher for every nine innings, with earned run describing a run scoring without a passed ball or defensive error (Wathen & Rhew, 2019). Strikeout is a statistical measure that reveals the extent to which a pitcher dominates and overpowers batters by retiring them following three strikes as judged by the umpire. ERA and strikeouts offer insights into the value of pitchers to the performance of a team (Wathen & Rhew, 2019). The resulting multiple regression equation used to investigate whether ERA and strikeouts are statistically significant predictor of the number of games won was: Number of games won = β0 + β1(ERA) + β2 (strikeout), where β0​, β1​, β2​ are coefficients to be estimated Following a data analysis, the developed model was statistically significant, F (2,27) = 12.27, p<0.001. A p-value of 0.00016 is well below the typical threshold of 0.05, meaning that the model developed is statistically significant. It is evident that a significant variation in the number of games won by a baseball team can be explained by ERA and strikeouts. The R-squared value generated for the model is 0.4761. The value suggests that around 47.61 percent of the variance in the number of games won can be effectively explained by the model. An adjusted R-squared value of 0.4373 indicates a slightly lower explanatory power of the model. Nonetheless, the generated R-value from the model would indicate that the model explains nearly half of the variance. From the model, it is evident that ERA predicts the number of games won (β =...