a. Using the results in column (1), what is the expected change in price of building a 500-square-foot addition to a house? Construct a 95% confidence interval for the percentage change in price. b. Comparing columns (2) and (3), is it better to use Size or ln(Size) to explain house prices?
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05. This means that for every 1 unit increase in Size (in this case, square footage), the price increases by 0.05 units. - Since we are looking at a 500-square-foot addition, the expected change in price would be 0.05 * 500 = 25 units. Show more…
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Suppose that a researcher collects data on houses that have sold in a particular neighborhood over the past year and obtains the regression results in the table shown below. Dependent variable: ln(Price) Regressor (1) (2) (3) (4) (5) Size 0.00052 (0.000038) ln(Size) 0.73 (0.058) 0.72 (0.091) 0.62 (2.06) 0.693 (0.061) ln(Size)^2 0.0085 (0.17) Bedrooms 0.0043 (0.037) Pool 0.082 (0.037) 0.077 (0.038) 0.085 (0.035) 0.091 (0.037) 0.086 (0.036) View 0.043 (0.034) 0.028 (0.032) 0.028 (0.033) 0.028 (0.029) 0.028 (0.031) PoolĂ—View 0.0027 (0.11) Condition 0.18 (0.049) 0.21 (0.035) 0.13 (0.038) 0.12 (0.039) 0.15 (0.036) Intercept 13.56 (0.073) 6.65 (0.41) 6.69 (0.56) 7.09 (7.52) 6.61 (0.42) Summary Statistics SER 0.108 0.103 0.101 0.105 0.109 R2 0.73 0.75 0.77 0.76 0.72 Variable definitions: Price = sale price ($); Size = house size (in square feet); Bedrooms = number of bedrooms; Pool = binary variable (1 if house has a swimming pool, 0 otherwise); View = binary variable (1 if house has a nice view, 0 otherwise); Condition = binary variable (1 if real estate agent reports house is in excellent condition, 0 otherwise). Using the results in column (1), what is the expected increase in price of building a 500-square-foot addition to a house, holding everything else in the model constant? The expected increase in price of building a 500-square-foot addition to a house is _% (Express your response as a percentage and round to two decimal places) The 95% confidence interval for the percentage change in price is _,_?
Madhur L.
Real estate investors would like to develop a regression model to predict the price of a house based on its characteristics (size, number of bedrooms, style, and so on). They have collected data on 88 houses sold in Portland, Oregon during the period from March 2018 to November 2018. The variables include price (house price in thousands of dollars), lot size (size of the lot in square feet), sqrft (size of the house in square feet), bdrms (number of bedrooms), and colonial (a dummy variable coded as 1 if the house is colonial style and 0 otherwise). The Excel output for the following model (Model 1) is given below: Model 1: price = intercept + lotsize + sqrft + bdrms + colonial ANOVA: MS Regression: 620279 Residual: 917855 Total: 1539134 Coefficients: Standard Error t Stat P-Value Intercept: 24.127 0.002 0.124 lotsize: 11.004 0.251 0.000 sqrft: 13.716 0.251 0.000 bdrms: 9.515 0.251 0.000 colonial: 14.637 0.251 0.000 Use Model 1 to answer the following parts: Interpret the coefficient of the variable colonial. The coefficient of the variable colonial is 14.637. This means that, holding all other variables constant, the mean price difference between houses of colonial style and houses of another style is $14,637. The mean price will increase by $14,637 for a one-unit increase in the variable "colonial", holding all other variables constant. What percentage of the variation in house price has been explained by the regression model?
Sri K.
A real estate Association in a suburban community would like to study the relationship between the size of a single-family house (as measured by number of rooms) and the selling price of the house (in thousands of dollars). Two different neighborhoods are included in the study, one on the east side of the community (=0) and the other on the west side (=1). A random sample of 20 houses was selected with the results given at left. a. State the multiple regression equation that predicts the selling price based on the number of rooms in the neighborhood. b. Interpret the regression coefficients in a. c. Predict the selling price for a house with nine rooms that is located in an East-side neighborhood. Construct a 95% confidence interval estimate and 95% prediction interval. d. Perform a residual analysis on the results and determine if the regression assumptions are valid. e. Is there a significant relationship between the selling price and two independent variables at the 0.05 level of significance? Price Rooms Neighborhood 309.6 7 0 307.4 8 0 340.3 9 0 346.5 12 0 298.2 6 0 337.8 9 0 324.1 10 0 313.2 8 0 327.8 9 0 325.3 8 1 308.5 6 1 361.3 13 1 337.4 10 1 346.2 10 1 342.4 9 1 323.7 8 1 329.6 8 1 343.6 9 1 360.7 11 1 348.3 9 1
Adi S.
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