Parametric Hypothesis

Nonparametric Hypothesis Testing for Major League Baseball

Nonparametric Hypothesis Testing for Major League Baseball

In Miami thousands of baseball fans, swarm their way inside the newly built Marlins Ballpark Stadium to cheer for their favorite baseball team. Americans are enamored with the sport. Fans enjoy the game because they can spend time with their friends and family on a sunny day and the simple sound of a cracking bat brings a smile to their faces. Major League Baseball consists of professional baseball players. Some of these players are paid huge salaries by the team owners, this ensures the best players will continue to play well and bring in the admiring fans. Strolling on the fields are some of the highest paid players, worth millions of dollars. Intrigued with the questions if the home run average of players is linked to their salaries, Team A decided to use the data sets provided of Major League Baseball home run average and player’s salary to conduct a nonparametric test. This article will include details of the nonparametric test of the formulated hypothesis, provide the five-step process involved, and describe the test outcome in comparison to previous research studies conducted by Team A.

Formulating the hypothesis

The data collection chosen for this paper is the Major League Baseball data set. The data collection included such variables as; the year the stadium was built, American or National League stadium capacity, total salary, whether the surface is natural or artificial, wins, attendance, earn run averages, batting averages, home runs, errors, year, stolen bases, and the average player’s salary. Team A will analyze the data set provided and will try to find the correlation, if any, between the number of home runs and the amount of money paid in salaries. Team A would disprove or prove the statement, and the formulated question would be: Is the home run average independent of the salary?

The...