Question

A reporter for a large sports television network wants to determine if "homefield advantage" is dependent upon the sport. He collects data from a sample of games played in the four major professional sports in the country, and he records the number of home team wins and visiting team wins for these games. Use the 0.10 significance level to test the claim that home/visitor team wins are dependent on the sport. Find the value of the P-VALUE that would be used in a hypothesis test of the claim.

Answer 1

Without the actual data, we cannot compute the exact p-value needed to test the independence between home/visitor team wins and the sport. However, the process would involve a chi-square test for independence with a significance level of 0.10.

Step-by-step explanation:

To determine if homefield advantage is dependent upon the sport, a hypothesis test for independence would typically be used. This involves a chi-square test for independence. Since the question specifies that we should use a significance level of 0.10, our alpha (α) is 0.10. If we were to perform this test and calculate the p-value, the p-value would indicate the probability of observing the data, or something more extreme, if the null hypothesis of independence (no relationship between home/visitor team wins and the sport) is true.

To make a decision, we compare the p-value to α. If the p-value is less than α, we reject the null hypothesis, suggesting that there is an association between home/visitor team wins and the sport. Without actual data from the reporter's study, we cannot compute the p-value, but the process would involve summing the observed and expected wins for home and visiting teams across each sport, then applying the chi-square test formula.

Answer 2

with 0.10 level of significance the P-VALUE that would be used in the hypothesis claim is 0.05%

Step-by-step explanation:

In hypothesis testing in statistics, we can say that the p-value is a probability of obtaining test results when we assume that the null hypothesis is correct.

The p-value is the probability that the null hypothesis is true.

A p-value less than or equals to 0.05 is statistically significant. It shows strong evidence against the null hypothesis, meaning there is less than a 5% probability the null is correct and clearly we can say that the results are random.