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) Variable is significant if p - value is less than 0.05.

So

i) Total rooms

ii) Total bathrooms are significant at 5% significance level.

B) t statistics is used to find the significance level. So, for each we can calculate critical 't' value and see if table 't' value if more or less.

Ctitical value for dof = 94 and 0.05 alpha = 1.986 or - 1.986

Significant if 't' table > 1.986 or 't' table < -1.986

C) Co efficient of Total Bathrooms = 43533.03

It means for 1 unit in increase of bathroom, sales price increases by 43533.03

D) Coefficient of Days on market = -72.53292

For every increase of 1 Day in market, there will be a decrease of -72.53 in home sale price.

E) R^2 = Coeeficient of determination = 0.33.

It means 33% of variance of dependent variable (Sales price) can be explained by the variance of independent variable.

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