2. The regression model below examines factors that impact the DV home sale pric

Question

2. The regression model below examines factors that impact the DV home sale price among recently sold homes in a suburban community. The IV is total rooms within the home, and the EVs include 1) total bedrooms, 2) total bathrooms, 3) whether the home has a basement or not, and 4) the total number of days the home was on the market prior to its sale. Please answer the following regarding the regression output below: A) Identify the variables that are statistically significant predictors (at the.05 percent level) of a home's sale price. [3)] B) Of the variables you identified in Part A, what statistics did you examine to determine each variable's statistical significance? Explain the thresholds used to determine statistical significance. [3] C) Explain what the coefficient for the EV "Total Bathrooms" means. [3] D) Explain what the coefficient for the EV "Days on Market" means. [3] E) What does the model's R-square of.33 mean? [3]

Explanation / Answer

Given, the regression model where the DV is home sale price, the IV is totalrooms and the EVs are bedrooms, bathrooms, basement and daysonmarket.

(A) The statistically significant predictors are totalrooms and bathrooms, at the 0.05 percent level of significance.
(B) In order to determine which predictors are statistically significant, I observed the p-values or the values which are present in the P>|t| column in the given regression output.
If the p-value is less than alpha = 0.05 (level of significance), then we can say that the corresponding predictor variable is statistically significant.
(C) The regression coefficient of the EV "Total Bathrooms" (bathrooms) is 43553.03. This means that if there is 1 more bathroom in a home compared to another home, then that home's sale price will increase by 43553.03.
(D) The regression coefficient of the EV "Days on Market" (daysonmarket) is -72.53292. This means that if any home was on the market for 1 more day prior to its sale, then the home sale price will decrease by 72.53.
(E) The R-square value of 0.33 means that 33% of the variation in the home sale prices can be explained by the regression of the DV home sale price on the IV, totalrooms, and the EVs - bedrooms, bathrooms, basement and daysonmarket.

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