Question

1. 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 percentlevel) of a home’s sale price. [3]

   B) Of the variables you identified in Part A, what statistics did you examine todetermine 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]

Answer 1

Answer:

(A)

Here, the regression for which p - value is less than 0.05, those regression or independent variables are significant in nature.

Here Total Rooms and bathrooms are the only significant factor here.

(B)

To evaluate the statistical significance of each variable, we can evaluate the t- value and the

p - value.

Here critical value of t - statistic is critical = t_0.05,90 = 1.9867

and either we can evaluate p = value (critical) = 0.05

so those variables who has absolute t value l t l < t_critical , those are not significant in nature.

(C)

Here Coefficient for the EV "Total bathrooms" mean that "If we increase one bathroom in the house that will increase the sale price of home by $ 43553.03".

(D)

Here coefficient for the EV "Days of Market " means that "if there is an increase of number of days the house was in the market prior to sale that will decrease its sales price by $ 72.53.

(E)

Here R- square of 0.33 mean that 33% of the variation in the sales price of the house is explained by the variation in the independent variables.