MAT 240 Module Five Assignment Template PDF

Preview

Summary

Module 5 Assignment...

Description

Hypothesis Testing for Regional Real Estate Company Hypothesis Testing for Regional Real Estate Company Rocco Louis Cannata Jr. Southern New Hampshire University

1

Hypothesis Testing for Regional Real Estate Company

2

Introduction I have been hired by the Regional Real Estate Company to help them analyze real estate data. One of the company’s Pacific region salespeople just returned to the office with a newly designed advertisement. It states that the average cost per square foot of his home sales is above the average cost per square foot in the Pacific region. He wants me to make sure he can make that statement before approving the use of the advertisement. The average cost per square foot of his home sales is $275. In order to test his claim, I have to collect a sample of 1,001 home sales for the Pacific region. Setup The population parameter is the cost per square foot in the Pacific region. The null hypothesis is the average cost per square foot of his home sales is equal to the average cost per square foot in the Pacific region. The alternative hypothesis is the average cost per square foot of his home sales is above the average cost per square foot in the Pacific region. The test that I will be using is the test distribution since the alternative hypothesis is expected to be greater than the null hypothesis. The test is left tailed. The significance level is .05. Data Analysis Preparations

Hypothesis Testing for Regional Real Estate Company

The The appropriate test statistic for this equation is T.DIS. My significance level for this equation is .05. The conditions to perform my identified test have been met. Calculations Since the graph is left tailed as the salesman believes that the sales are greater than the national average. Ho) M≤275 Ha) M>275. To solve for the p value, we first must solve for t. To

3

Hypothesis Testing for Regional Real Estate Company

4

solve for t, we first must come up with the equation, which is (264.0673-275)/5.117495. (10.9327)/5.117495 and the answer to that is -2.136336102 which would be our test statistic. To receive the degree of freedom, our sample size is 1001. So, we must calculate 1001-1 to receive 1000 for our degree of freedom. When calculating for the p value, we must use Microsoft excel; by selecting a random cell and adding =T. DIST(-2.136336102,1000,True) and hitting enter, we received 0.016447136 which is our p value. If I were graphing the test statistic and the p value on a bell shape graph, it would be left tailed and fall into the 99.7 percentile of the graph. Test Decision The p value (0.016447136) is less than the significance level (.05) which I would have to reject the null hypothesis. Conclusion We can conclude that salesman was correct with his prediction in saying that the average cost per square foot of his home sales is above the average cost per square foot in the Pacific region. The reason being is that since P-value is less than 0.05, we rejected the H0 and we have the evidence in the claim HA that the sales are higher than 275. So, since the salesman hypothesis was concluded to be correct, the conclusion was statistically significant....