IntroductionThe Major League Baseball (MLB) organization is a group of baseball teams that have reached the Major League. The Major League Baseball dataset provides 2005 salaries for several Major League Baseball (MLB) teams, as well as individual player salaries for 30 teams (Lind, Marchal, & Wathen, 2008). The MLB dataset provides information such as batting averages, wins, salaries, home runs, errors, and more. (Lind, Marchal & Wathen, 2008). Two specific teams stand out in the news when looking at their stats; St. Louis and Kansas City. These two teams are radically different; one has the most wins among the MLB dataset and the other has the fewest wins. Since St. Louis and Kansas City are both major leaguers, they should be considered good, which leads us to wonder if salaries play a role in whether a team is more successful than a other. We will examine team scores as well as individual scores within both teams to investigate whether salaries affect the quality of performance. In this article, we will perform a regression test to determine whether salaries affect the performance of St. Louis and Kansas City. Hypothesis Statement There are many differences between the two samples in the dataset; we start with the national and American league. In our dataset, salary affects player performance based on wins and losses. How does salary affect team batting average? How does salary affect ERA teams? Kansas City has a salary of 36.9 million and his batting average is .263 and his ERA is 5.49. St. Louis has a salary of 92.1 million and his batting average is .270 and his ERA is 3.49. Is there a correlation between batting average and ERA based on each team's salary? In the data set, the middle of the article... ANOVA is a procedure in which the total variability of a random variable is subdivided into components so that it can be better understood or attributed to each. from the various sources which cause this figure to vary. Applied to regression parameters, ANOVA techniques are used to determine the usefulness of a regression model and the extent to which changes in an independent variable example, we can formulate a hypothesis: testing procedure to determine whether the slope coefficients are equal to zero (the variables are not related) or whether the relationship has statistical significance (the slope b is different from zero). An F test can be used for this process.ConclusionReferencesLind, Marchal and Wathen. (2008). Statistical Techniques in Business and Economics, 13th Edition. New York, New York: McGraw-Hill