IntroductionThe Major League Baseball (MLB) organization is a group of baseball teams that have made it to the Major Leagues. The Major League Baseball dataset provides the 2005 salaries of multiple Major League Baseball (MLB) teams, as well as individual player salaries on 30 teams (Lind, Marchal & Wathen, 2008). The MLB dataset provides information such as batting averages, wins, salaries, home runs, errors, etc. (Lind, Marchal & Wathen, 2008). Two specific teams stand out from the information when looking at their statistics; St. Louis and Kansas City. These two teams are drastically different; one has the most wins in the MLB dataset and the other has the fewest wins. Since St. Louis and Kansas City are both in the major leagues, they must be considered good, which makes us wonder if salaries play a role in whether one team is doing better than another. We will look at team scores and individual scores within the two teams to see if salaries impact the quality of performances. In this article we will conduct a regression test to see if salaries affect the performance of St. Louis and Kansas City. Hypothesis Statement There are many differences in the two samples of the data set; Let's start with the National and American League. In our dataset, salary influences player performance based on wins and losses. How does salary affect teams batting average? How does salary affect team ERA? Kansas City has a salary of 36.9 million, the batting average is .263 and the ERA is 5.49. St. Louis has a salary of 92.1 million, the batting average is .270 and the ERA is 3.49. Is there a correlation between batting average and ERA based on each team's salary? In the data set...... half of the document...... ANOVA, is a procedure in which the total variability of a random variable is divided into components so that it can be better understood, or attributed to each of the various sources that make the number vary. Applied to regression parameters, ANOVA techniques are used to determine the usefulness of a regression model and the degree to which changes in an independent variable X can be used to explain changes in a dependent variable Y. For example, we can conduct a hypothesis: Testing procedure to determine whether the slope coefficients are equal to zero (the variables are not correlated) or whether there is statistical significance in the relationship (the slope b is non-zero). An F test can be used for this process.ConclusionsReferencesLind, Marchal and Wathen. (2008). Statistical Techniques in Business & Economics, 13th edition. New York, NY: McGraw-Hill