For this project, you will construct a regression model to explain the selling price of single- family houses in Waltham. The structure of the model will be entirely up to you. Your model will be evaluated based on how well it fits the data, as measured primarily by its statistical significance and its ability to explain price variability (r2).
Your completed project should be in the form of a written management report comprised of:
(1) an executive summary of no more than one page in which you explain your model and the variables you have used, and (2) a technical appendix in which you show and explain your statistical results, including summary-level R output. Include these R regression summaries as screenshots from the R Studio console (lower-left quadrant of the R Studio screen). Any variable transformations you may have used should be clearly described. The detailed
explanation of your model should be limited to no more than four pages, so that the text portion of your project, including the executive summary, consists of no more than 5 pages in total.
Your statistical analysis must include:
• A list, with definitions of all predictors (independent variables) in your model.
• Summary level R Model Output showing coefficients, F and t statistics.
• Significance tests for overall model significance (F test) and significance tests for each predictor in your model (t tests).
• A 95% confidence interval and a 95% prediction interval for a house with xi values suited to your model which you select to represent a Waltham house with a profile to form the basis of interval estimates.
• For at least one predictor, a 95% confidence interval for its regression coefficient.
Remember to exclude houses selling for $1 from your model.
All data transformations should be done in R, without deleting any data from the file. Remember, this is linear regression. Each of your predictors must be properly scaled. Categorical variables must be in (0,1) form and may require transformation [ifelse()] before use in your model.
Creativity and style count, but the explanatory power of your model and your ability to clearly explain it to a management audience are of primary importance.
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