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. 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

1) Before looking at the data, which area do you think will have higher home prices, and why? 2) Include a Histogram and list the average sales price for each. 3) Provide a scatter plot against price for each variable. 4) Are the relationships linear? 5) 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. 6) 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. 7) 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.

8) 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? 9) 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? 10) Which model performed the best of three you ran?