You 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. The average cost per square foot of home sales based on this advertisement is $280. The salesperson claims that the average cost per square foot in the Pacific region is less than $280. In other words, he claims that the newly designed advertisement would result in higher average cost per square foot in the Pacific Region. He wants you to make sure he can make that statement before approving the use of the advertisement. In order to test his claim, you will generate a random sample of size 750 houses using data for the Pacific region and use this data to perform a hypothesis test.

Population parameter=mean dollars per square foot in the Pacific region
Ho: mean = 280
Ha: mean <280
significance level = 0.05 allows 5% error
t-test
left tailed test
	Cost per Square foot
Mean	$267
Medain	$204
Std dev	166.3489553
std Error	6.074205017
test stat	-2.140197765
p value	0.016330591
test results
0.02 <0.05
decision
reject Ho. There is sufficient evidence to support the claim that the mean cost per sq ft in the Pacific region is less than 280.

1. describe the p value and test statistic in regard to the normal curve graph, discuss how the p value relates to the significance level, and compare the p value to the significance level to reject or fail to reject the null hypothesis.

2. Using the normal curve graph as a reference, describe where the p value and test statistic would be placed.

• 3. Test Decision: Compare the relationship between the p value and the significance level, and decide to reject or fail to reject the null hypothesis.
  • Compare the relationship between the p value and significance level.
  • Decide to reject or fail to reject the null hypothesis.
• 4. Conclusion: Discuss how your test relates to the hypothesis and discuss the statistical significance.
  • Explain in one paragraph how your test decision relates to your hypothesis and whether your conclusions are statistically significant.