Category: uncategorised

Scenario

You have been hired by the Regional Real Estate Company to help them analyze real estate data. One of the companys Pacific region salespeople is working to design a new advertisement. The initial draft of the advertisement states that the average cost per square foot of home sales in the (Pacific region) is $280. The salesperson claims that the average cost per square foot in the Pacific region is less than $280. He wants you to make sure he can make that statement (that the average cost per square foot is less than $280) before asking for the advertisement text to be changed. In order to test his claim, you will generate a random sample size of 750 using data for the (Pacific region) and use this data to perform a hypothesis test.

Prompt

Generate a sample size of 750 houses using data for the (Pacific region). Then, design a hypothesis test and interpret the results using significance level = .05. You will work with this sample in this assignment. Briefly describe how you generated your random sample.

Use the document attached below to help support your work on this assignment. You may also use the and tutorials for support.

Specifically, you must address the following rubric criteria:

• Introduction: Describe the purpose of this analysis and how you generated your random sample size of 750 houses.
• Hypothesis Test Setup: Define your population parameter, including hypothesis statements, and specify the appropriate test.
• Define your population parameter.
• Write the null and alternative hypotheses.
• Specify the name of the test you will use.
• Identify whether it is a left-tailed, right-tailed, or two-tailed test.
• Data Analysis Preparations: Describe sample summary statistics, provide a histogram and summary, check assumptions, and identify the test significance level.
• Provide the descriptive statistics (sample size, mean, median, and standard deviation).
• Provide a histogram of your sample.
• Summarize your sample by writing a sentence describing the shape, center, and spread of your sample.
• Check whether the assumptions to perform your identified test have been met.
• Identify the test significance level. For example, = .05.
• Calculations: Calculate the p value, 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.
• Calculate the sample mean and standard error.
• Determine the appropriate test statistic, then calculate the test statistic.
• Note: This calculation is (mean target)/standard error. In this case, the mean is your regional mean (Pacific), and the target is 280.
• Calculate the p value using one of the following tests.
• Choose your test from the following:
• =T.DIST.RT([test statistic], [degree of freedom]): right-tailed test
• =T.DIST([test statistic], [degree of freedom], 1): left-tailed test
• =T.DIST.2T([test statistic], [degree of freedom]): two-tailed test
• Note: The degree of freedom is calculated by subtracting 1 from your sample size.
• Using the normal curve graph as a reference, describe where the p value and test statistic would be placed.
• 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.
• 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.