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TBUS 301 Case Study Home Price Analysis Case Study Multiple Regression Introducing the Scenario Real Estate can be a highly competitive market, and having every advantage is very important. Margaret Robinson, a local real estate agent, invests in statistical investigation in order to improve her position in the market, and do the best job she can for her clients, all with style and class. Margaret has hired you (and your team) to utilize the Redfin website, which provides historical data, in order to ask some important questions about prices and predictions of prices in different areas that she services. You will pick two zipcodes, in Washington, to explore in this assignment. You will download data from the Redfin site into Excel. Videos and instructions for how to do this posted on Canvas. You will then use the R code, making appropriate changes, in order to use the data you gathered. The following variables will be used: Price Bedrooms Bathrooms Square Footage Lot Size Year Built Part 1: 1) Before looking at the data, which area do you think will have higher home prices, and why? 2) Include two Histograms, one for each area, and list the average sales price for each. 3) Was your assumption about which area had higher prices in question 1 correct? 4) Provide a scatter plot against price for each variable. 5) Are the relationships linear? 6) Use Statistical inference to test the claim that the square foot of each of your areas is different than 2000 Is there sufficient evidence in either of your areas to say that the average square footage is different than 2000? State which ones and why. 7) Use statistical inference to test the claim that the number of bathrooms in each of your areas is greater than 3. Is there sufficient evidence in either of your areas to say that the number of bathrooms in each of your areas is greater than 3. State which ones and why. Part 2: You will use the same data as before, but you will combine these datasets into a single dataset. Additionally, you need to add a dummy variable for zipcode, in order to differentiate between the two zipcodes. The following variables will be used: Price Bedrooms Bathrooms Square Footage Lot Size Year Built Zip Dummy 1) Estimate a regression with home sales price as the dependent variable and bedrooms, bathrooms, sqft, and year built as explanatory variables with the data that includes both zip codes. Do not include the Zip Dummy. 2) Run the same regression as before, but include your Zip Dummy variable. Report your regression output. 3) The coefficient on the dummy variable represents the average price difference in the area you identified as a “1” in your dataset. What is the average price difference in this zip code. Write this in plain English. 4) . Run the VIF (collinearity) test on your regression. a. Is there any collinearity in your model? b) if there were collinearity, what should you do? 5) a. Display the histograms of sales prices and square footage below. b. Comment on whether the distributions are skewed or not. c. Generate the log of sales price and square footage. d. Display the two histograms of the log of sales price and square footage. e. How have the distributions changed? 6) a. Run a regression with the logged values. Place the results below. b. How do you interpret the coefficient on Beds? c. What about the log of square feet? 7) Which model performed the best of three you ran? 11/28/2020 Home Price Analysis Case Study https://canvas.uw.edu/courses/1402592/assignments/5736446?module_item_id=11385809 1/2 Home Price Analysis Case Study Due Dec 6 by 11:59pm Points 100 Submitting a file upload TBUS 301 Case Study Rubric (1) Submit Assignment Case overview and questions: Home Price Analysis Case Study.docx Submission Instructions: For this case study you will be submitting three files: 1) Your copy of the initial case word document edited to include answers to the questions 2 and 3) The two Excel files you downloaded from Redfin https://canvas.uw.edu/courses/1402592/files/67991324/download?wrap=1 11/28/2020 Home Price Analysis Case Study https://canvas.uw.edu/courses/1402592/assignments/5736446?module_item_id=11385809 2/2 Total Points: 100.0 Criteria Ratings Pts 25.0 pts 25.0 pts 50.0 pts Professionalism Submission was on time, well formatted, and clearly communicated the solutions to the problems. 25.0 to >13.33 pts Full Marks 13.33 to >0.0 pts Half Marks 0.0 pts No Marks Correct Use of Tools and Techniques Solutions were obtained using the tools and techniques as laid out in the instructions. The approach followed was logical and where necessary was supplemented with well documented assumptions. Submission demonstrates that sufficient time and effort were given to the assignment. 25.0 to >13.33 pts Full Marks 13.33 to >0.0 pts Half Marks 0.0 pts No Marks Accuracy Answers match the answer key produced by the instructor. Any assumptions made that lead to differences from the instructor's answer key were clearly identified. 50.0 to >25.0 pts Full Marks 25.0 to >0.0 pts Half Marks 0.0 pts No Marks