Data were collected from a random sample of 210 home sales from a community in 2003. Let Price denote the selling price (in $1,000), BDR denote the number of bedrooms, Bath denote the number of bathrooms, Hsize denote the size of the house (in square feet), Lsize denote the lot size (in square feet), Age denote the age of the house (in years), and Poor denote a binary variable that is equal to 1 if the condition of the house is reported as "poor." An estimated regression yields Price = 113.2 +0.461BDR+ 22.2Bath + 0.148Hsize +0.003Lsize +0.086Age 46.4Poor, Ŕ² = 0.68, SER=39.4. Suppose that a homeowner converts part of an existing family room in her house into a new bathroom. What is the expected increase in the value of the house? The expected increase in the value of the house is $ 22200. (Round your response to the nearest dollar.) Suppose that a homeowner adds a new bathroom to her house, which increases the size of the house by 95 square feet. What is the expected increase in the value of the house? The expected increase in the value of the house is $ 36260. (Round your response to the nearest dollar.) What is the loss in value if a homeowner lets his house run down so that its condition becomes "poor"? The loss is $ 46400. (Round your response to the nearest dollar.) Compute the R² for the regression. The R² for the regression is (Round your response to three decimal places.) A professor is interested in understanding the factors on which the grades a student obtains in her final exam depends. She chooses the student's intelligence quotient (X1), the grades obtained in the mid-term exam (X2), the number of days the student was present in the class in the whole semester (✗3) as regressors. She randomly selects 150 students and estimates the following regression function: Ŷ₁ = 49+ 1.23X₁; +0.45X2; + 0.98X3¡ + U¡, where ŷ;, X1, X2;, and X3; denote the predicted value of grades obtained by the ith student, her intellegence quotient, the grades obtained by her in the mid-term exam, and the number of days she was present in the class in the whole semester, respectively. She calculates the value of the regression R² to be 0.78. The value of the adjusted R² (R²) will be ☐. (Round your answer to two decimal places). Which of the following statements is not true about the R²? (Check all that apply.) of R² can be negative. A. The value of B. The value of Ŕ² increases when a new regressor is added to the regression equation. C. The value of R² always lies between 0 and 1. D. The value of R² is always greater than the R² given that the number of regressors remains the same in the regression equation.