Explain Which Parametric Test

Explain which nonparametric test you used to analyze your data and why you chose that test 250-300

Our team goal for this paper is to determine if earned run averages are independent of wins in baseball teams. We decided that a non-parametric test would be best suited to accomplish this goal. Non-parametric tests are tests that do not make assumptions about the population. All of the most commonly used nonparametric tests rank the outcome variable from low to high and then analyze the rank. In parametric testing, the data is not required to fit a normal distribution. It uses data that does not always rely on numbers, instead relying on a ranking or order of sorts.

The data set we decided to use for testing was total wins and earned runs average by team, in baseball teams in 2005. To help us analyze our data and reach an answer to our question, we decided to use a non-parametric test called the Chi Square test. This test is used to determine independence. It is a measurement of how expectations compare to the results. This test is mostly used to compare observed data against data that is expected from a specific hypothesis. The data used to calculate a chi square test is random, raw, and taken from independent variables pulled from a big enough sample. An example of this would be the results of a coin being tossed 100 times. The chi-square test is always testing what is called the null hypothesis, which states that there is no significant difference between an observed result and the expected result. Due to our small sample data and null hypothesis, we are confident that is the most appropriate and efficient test for our analysis.