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7-3 Project Two Submission

Coursework Instructions:
Competency In this project, you will demonstrate your mastery of the following competency: Apply statistical techniques to address research problems Perform hypothesis testing to address an authentic problem Overview In this project, you will apply inference methods for means to test your hypotheses about the housing sales market for a region of the United States. You will use appropriate sampling and statistical methods. Scenario You have been hired by your regional real estate company to determine if your region’s housing prices and housing square footage are significantly different from those of the national market. The regional sales director has three questions that they want to see addressed in the report: Are housing prices in your regional market lower than the national market average? Is the square footage for homes in your region different than the average square footage for homes in the national market? For your region, what is the range of values for the 95% confidence interval of square footage for homes in your market? You are given a real estate data set that has houses listed for every county in the United States. In addition, you have been given national statistics and graphs that show the national averages for housing prices and square footage. Your job is to analyze the data, complete the statistical analyses, and provide a report to the regional sales director. You will do so by completing the Project Two Template located in the What to Submit area below. Directions Introduction Region: Start by picking one region from the following list of regions: West South Central, West North Central, East South Central, East North Central, Mid Atlantic Purpose: What is the purpose of your analysis? Sample: Define your sample. Take a random sample of 500 house sales for your region. Describe what is included in your sample (i.e., states, region, years or months). Questions and type of test: For your selected sample, define two hypothesis questions (see the Scenario above) and the appropriate type of test for each. Address the following for each hypothesis: Describe the population parameter for the variable you are analyzing. Describe your hypothesis in your own words. Identify the hypothesis test you will use (1-Tail or 2-Tail). Level of confidence: Discuss how you will use estimation and confidence intervals to help you solve the problem. 1-Tail Test Hypothesis: Define your hypothesis. Define the population parameter. Write null (Ho) and alternative (Ha) hypotheses. Note: For means, define a hypothesis that is less than the population parameter. Specify your significance level. Data analysis: Summarize your sample data using appropriate graphical displays and summary statistics and confirm assumptions have not been violated to complete this hypothesis test. Provide at least one histogram of your sample data. In a table, provide summary statistics including sample size, mean, median, and standard deviation. Note: For quartiles 1 and 3, use the quartile function in Excel: =QUARTILE([data range], [quartile number]) Summarize your sample data, describing the center, spread, and shape in comparison to the national information (under Supporting Materials, see the National Summary Statistics and Graphs House Listing Price by Region PDF). Note: For shape, think about the distribution: skewed or symmetric. Check the conditions. Determine if the normal condition has been met. Determine if there are any other conditions that you should check and whether they have been met. Note: Think about the central limit theorem and sampling methods. Hypothesis test calculations: Complete hypothesis test calculations. Calculate the hypothesis statistics. Determine the appropriate test statistic (t). Note: This calculation is (mean – target)/standard error. In this case, the mean is your regional mean, and the target is the national mean. Calculate the probability (p value). Note: This calculation is done with the T.DIST function in Excel: =T.DIST([test statistic], [degree of freedom], True) The degree of freedom is calculated by subtracting 1 from your sample size. Interpretation: Interpret your hypothesis test results using the p value method to reject or not reject the null hypothesis. Relate the p value and significance level. Make the correct decision (reject or fail to reject). Provide a conclusion in the context of your hypothesis. 2-Tail Test Hypotheses: Define your hypothesis. Define the population parameter. Write null and alternative hypotheses. Note: For means, define a hypothesis that is not equal to the population parameter. State your significance level. Data analysis: Summarize your sample data using appropriate graphical displays and summary statistics and confirm assumptions have not been violated to complete this hypothesis test. Provide at least one histogram of your sample data. In a table, provide summary statistics including sample size, mean, median, and standard deviation. Note: For quartiles 1 and 3, use the quartile function in Excel: =QUARTILE([data range], [quartile number]) Summarize your sample data, describing the center, spread, and shape in comparison to the national information. Note: For shape, think about the distribution: skewed or symmetric. Check the assumptions. Determine if the normal condition has been met. Determine if there are any other conditions that should be checked on and whether they have been met. Note: Think about the central limit theorem and sampling methods. Hypothesis test calculations: Complete hypothesis test calculations. Calculate the hypothesis statistics. Determine the appropriate test statistic (t). Note: This calculation is (mean – target)/standard error. In this case, the mean is your regional mean, and the target is the national mean.] Determine the probability (p value). Note: This calculation is done with the TDIST.2T function in Excel: =T.DIST.2T([test statistic], [degree of freedom]) The degree of freedom is calculated by subtracting 1 from your sample size. Interpretation: Interpret your hypothesis test results using the p value method to reject or not reject the null hypothesis. Compare the p value and significance level. Make the correct decision (reject or fail to reject). Provide a conclusion in the context of your hypothesis. Comparison of the test results: Revisit Question 3 from the Scenario section: For your region, what is the range of values for the 95% confidence interval of square footage for homes? Calculate and report the 95% confidence interval. Show or describe your method of calculation. Final Conclusions Summarize your findings: In one paragraph, summarize your findings in clear and concise plain language. Discuss: Discuss whether you were surprised by the findings. Why or why not? You can use the following tutorial that is specifically about this assignment: MAT-240 Module 7 Project Two Video What to Submit To complete this project, you must submit the following: Project Two Template Word Document Use this template to structure your report, and submit the finished version as a Word document. Supporting Materials The following resources may help support your work on the project: Data Set: MAT 240 House Listing Price by Region Spreadsheet Use this data for input in your project report. Document: National Summary Statistics and Graphs House Listing Price by Region PDF Use this data for input in your project report. Use these tutorials for support with the Excel functions you will use in the project: Tutorial: Random Sampling in Excel PDF Tutorial: Scatterplots in Excel PDF Tutorial: Descriptive Statistics in Excel PDF Tutorial: Creating Histograms in Excel PDF
Coursework Sample Content Preview:
Report: Regional vs. National Housing Price Comparison [Your Name] Southern New Hampshire University Introduction Region: Mid-Atlantic region Purpose: The objective of this paper is to examine the house listing prices and cost per sq. ft with reference to a sample of house sales in the Mid-Atlantic area. This analytical work will try to give a general overview of the housing market in this area and assess certain hypotheses about house prices and costs. Sample: The sample which was selected from 1001 data listing using the Rand()function in Excel comes from key states such as NY, NJ, and PA. From each state there are counties and some of them include Washington, Litchfield, Penobscot, and Merrimack, among others. Data is characterized by house listing prices, square footage for each house sold, and cost per square foot. Questions and type of test First Hypothesis The average price listing in this region (Mid-Atlantic) is different from the national average house price listing. Using a Two-Tail Test we will test and either accept or reject the hypothesis * H0: The average house listing price in the Mid-Atlantic region is the same as the national average house listing prices. * H1: The average house prices that are listed for sale in the Mid-Atlantic region are different from the house listing prices nationally. Second Hypothesis We hypothesize that the average cost per square foot in the Mid-Atlantic region is higher than the national average cost per square foot. Using a 1-tail tail we test the following: * H0: In the Mid-Atlantic region, the average cost per sq. of the cost per square foot is less or comparable to the national average. * H1: In the Mid-Atlantic region, the average cost per sq. foot is more than the total average cost per foot nationally. Level of confidence To answer these hypotheses, the estimation and confidence intervals will be used. The result of a confidence interval will give us a range within which we believe the true parameter of the population falls with some degree of certainty. Our desired confidence level for this analysis will be 95%. This implies that it is possible to have up to a 95% level of confidence that the true mean is contained in the calculated confidence interval. 1-Tail Test We use a 1-tail test for hypothesis 1. Data analysis From the histogram data, it is evident that the majority of the houses sell between $207,800 to $271800. 6 houses sell for as high as $783,800 and are therefore potential outliers in the dataset. Data analysis From the data mean house price listin...
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