This is all the information. I have done all the calculations and what was inquired. I do need some help in putting it in word form. ( I did everything except for the bottom part where it says "Final Conclusions". In order to properly document the actions, I require assistance.

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:

1. Are housing prices in your regional market lower than the national market average?
2. Is the square footage for homes in your region different than the average square footage for homes in the national market?
3. 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 show the Project Two Template located in the What to Submit area below.

Directions

Introduction

1. 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
2. Purpose: What was the purpose of your analysis, and what is your approach?
   1. Define a random sample and two hypotheses (means) to analyze.
3. Sample: Define your sample. Take a random sample of 500 observations for your region.
   1. Describe what is included in your sample (i.e., states, region, years or months).
4. Questions and type of test: For your selected sample, define two hypothesis questions and the appropriate type of test hypothesis for each. Address the following for each hypothesis:
   1. Describe the population parameter for the variable you are analyzing.
   2. Describe your hypothesis in your own words.
   3. Describe the inference test you will use.
      1. Identify the test statistic.
5. Level of confidence: Discuss how you will use estimation and confidence intervals to help you solve the problem.

1-Tail Test

1. Hypothesis: Define your hypothesis.
   1. Define the population parameter.
   2. Write null (Ho) and alternative (Ha) hypotheses.
   3. Specify your significance level.
2. Data analysis: Analyze the data and confirm assumptions have not been violated to complete this hypothesis test.
   1. Summarize your sample data using appropriate graphical displays and summary statistics.
      1. Provide at least one histogram of your sample data.
      2. In a table, provide summary statistics including sample size, mean, median, and standard deviation.
      3. Summarize your sample data, describing the center, spread, and shape in comparison to the national information.
   2. Check the conditions.
      1. Determine if the normal condition has been met.
      2. Determine if there are any other conditions that you should check and whether they have been met.
3. Hypothesis test calculations: Complete hypothesis test calculations, providing the appropriate statistics and graphs.
   1. Calculate the hypothesis statistics.
      1. Determine the appropriate test statistic (t).
      2. Calculate the probability (p value).
4. Interpretation: Interpret your hypothesis test results using the p value method to reject or not reject the null hypothesis.
   1. Relate the p value and significance level.
   2. Make the correct decision (reject or fail to reject).
   3. Provide a conclusion in the context of your hypothesis.

2-Tail Test

1. Hypotheses: Define your hypothesis.
   1. Define the population parameter.
   2. Write null and alternative hypotheses.
   3. State your significance level.
2. Data analysis: Analyze the data and confirm assumptions have not been violated to complete this hypothesis test.
   1. Summarize your sample data using appropriate graphical displays and summary statistics.
      1. Provide at least one histogram of your sample data.
      2. In a table, provide summary statistics including sample size, mean, median, and standard deviation.
      3. Summarize your sample data, describing the center, spread, and shape in comparison to the national information.
   2. Check the assumptions.
      1. Determine if the normal condition has been met.
      2. Determine if there are any other conditions that should be checked on and whether they have been met.
3. Hypothesis test calculations: Complete hypothesis test calculations, providing the appropriate statistics and graphs.
   1. Calculate the hypothesis statistics.
      1. Determine the appropriate test statistic (t).
      2. Determine the probability (p value).
4. Interpretation: Interpret the hypothesis test results using the p value method to reject or not reject the null hypothesis.
   1. Relate the p value and significance level.
   2. Make the correct decision (reject or fail to reject).
   3. Provide a conclusion in the context of the hypothesis.
5. Comparison of the test results: See Question 3 from the Scenario section.
   1. Calculate a 95% confidence interval. Show or describe the method of calculation.
   2. Interpret a 95% confidence interval.

Final Conclusions

1. Summarize your findings: Refer back to the Introduction section above and summarize the findings of the sample that was selected.
2. Discuss: Talk about whether you were surprised by the findings. Why or why not?

Summary Statistics for MAT 240 House Listing Price by Region (for dataset in Modules 5 and 7) n Mean Median Std. Dev. Min Q3 Max Listing 9,009 288,407 249,940 172, 779 60,670 182,725 334,640 2,283,050 price ($) Cost per 9,009 152 126 121 41 99 164 2,486 square foot ($) Square 9,009 1,944 1,899 385 343 1,717 2,132 4,646 feet This graph shows the frequency for listing price. National Listing Price 3500 3000 2500 2000 1500 1000 500 10, 100000 (100000, 200000] (200090, 300000] (309090, 4000001 (409090, 5009001 1500000, 609050) (600000, 706000 1700909, BOODOO! 800400, 9800601 This graph shows the frequency for square feet. National Square Feet 4000 3500 3000 2500 2000 2500 1000 500 10 90, 37530 1875 00/750.60 1750 09/ 1125 00) 12125.60, 1500 09) (1500 09/1875,00 12875.09/2259 00) (2625 00/3090 60) X HCost per Square House listing square footage price foot Random Region National Average Listing Price $288,407 State County $165,045 $95 1,730 0.285187862 $225,496 AR lonoke West South Central Sample Mean W N $219,950 $116 1,899 0.719786483 71236.96806 LA livingston West South Central $89 1,847 0.752995576 Sample Std Dev TX potter West South Central $165,050 3185.814062 LA $158,300 Standard Error vernon West South Central $70 2,266 0.804993929 TX webb West South Central $180,764 $101 1,784 0.046058505 OK payne West South Central $175,050 $96 1,831 0.179286805 8 AR washington West South Central $286,050 $126 2,275 0.302087227 9 HO: u = 288,407 OK tulsa West South Central $245,050 $10 2,286 0.857923068 10 OK washington West South Central $142,835 $69 2,083 0.891297052 H1: u