Questions 13-14 depend on the information presented below. Here is the Excel output from regressing the list price of a home (Price, measured in thousands of dollars) on the size (SquareFootage, measured in square feet) and number of bedrooms (NumberBedrooms). The regression equation and the results are: Price - 96.7 + 2.67SquareFootage -1159.89NumberBedrooms Independent Variable Coefficients Standard Error -Stat p-value Intercept 96.70134 1120.035 0.086338 0.932622 NumberBedrooms -1.15989 377.873 3.06953 0.009725 SquareFootage 2.672517 0.169688 15,74963 2.22E-09 D Question 13 1 pts Which of the following variables (not including the intercept) are statistically significant predictors of house price? Use a 5% level. (Use the data in the prompt above.) Both SquareFootage and NumberBedrooms O Only NumberBedrooms Only SquareFootage Neither O Cannot be determined from the information givenQuestion 14 1 pts Suppose that a homeowner decides to remove a bedroom from their home, without changing the square footage (e.g. by converting the existing space). Which of the following BEST describes how they should expect the value of their home to change as a result of this addition? (Use the data in the prompt above.) The value of the home is expected to increase by about $1,159.89. The value of the home is expected to decrease by about $1,159.89 Since the p-value for Number Bedrooms indicates this variable is not significant, this model cannot be used to draw any conclusions here. Since regression models are not able to model the causal effect of external manipulations, this model cannot be used to draw any conclusions here