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

A) Statistically Significant variables (at .05 percent level) - The variables with p values less than .05 are the significant variables for the level of significance given in the question. The variables are Total rooms and Bathrooms

B) P value is used to measure the statistical significance of the variable. The threshold used is alpha = .05, which shows 95% confidence interval.

C) The coefficient of total bathroom is positive which means that the price (dependant variable) increase with the number of bathrooms of the house

D) The coefficient for daysonmarket is negative, which means that the price of the house will come down when the property is listed for more days

E) R-squared will give percentage of the total variance in the dependant variable that is explained by independant variable. In this example it shows that the independent variables explains 33% of the variance in the price changes.

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