Guidelines:
- Maximum of 10 pages, including all text, graphics, and tables.
- Use any software you choose (Excel, R, or other statistical tools).
- The focus is on statistical analysis combined with well-phrased paragraphs that include an introduction, a description of your approach, and an interpretation of your results.
- Include relevant visualizations (graphs, tables) directly in your document. If using R, append the R code as an appendix (this is not included in the page limit).
- The project is individual, but brainstorming with peers is encouraged.
Deadline: Sunday, 27 October at 23:59.
Project:
You are advising a real estate agent who is trying to understand the pricing of homes in her region, which includes small to midsize towns and a small city. The data comes from 1,200 recently sold homes, which contain the following variables:
- Sale Price (in $)
- Lot size (in acres)
- Waterfront (Yes, No)
- Age (in years)
- Central Air (Yes, No)
- Fuel Type (Wood, Oil, Gas, Electric, Propane, Solar, Other)
- Condition (1 to 5, 1 = Poor, 5 = Excellent)
- Living Area (in square feet)
- Pct College (% of people in the ZIP code who attend a four-year college)
- Full Baths (number of full bathrooms)
- Half Baths (number of half bathrooms)
- Bedrooms (number of bedrooms)
- Fireplace (Yes, No)
Instructions:
Select 3 out of the following 5 questions to answer. Focus on providing detailed analysis and clear interpretations. You can use confidence intervals, hypothesis testing, or any appropriate statistical tools.
-
Advising on Four-Bedroom House Pricing:
Use confidence intervals to estimate the average sale price for four-bedroom houses in the region. Then, compare this with the confidence interval for two-bedroom homes. What can you infer about pricing based on the number of bedrooms? -
Central Air Conditioning’s Impact on Sale Price:
Investigate how central air conditioning affects home prices. Begin by focusing on two-bedroom homes and compare the average price of homes with and without central air. Repeat the analysis for four-bedroom homes. What are your conclusions? -
Fireplace and Home Value:
Analyze how having a fireplace influences the sale price. Perform this analysis separately for both two-bedroomand four-bedroom homes. Provide confidence intervals for homes with and without fireplaces. What patterns do you notice? -
Categorical Factors and Home Sale Price:
Explore how other categorical factors (such as Waterfront status, Fuel Type, or Condition) impact the sale price. Formulate hypotheses and use hypothesis testing to draw conclusions. Which factors appear to have the strongest influence? -
Additional Insights:
Consider any other questions that may help the real estate agent in better understanding the data. This could involve investigating interactions between variables or analyzing specific subgroups (e.g., homes in excellent condition, older homes, etc.). Summarize any further insights.