Sales of single-family houses have been brisk in MidCity this year. This has especially been true...

 

  

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Sales of single-family houses have been brisk in MidCity this year. This has especially been true in older, more established neighborhoods, where housing is relatively inexpensive compared to the new homes being built in the new neighborhoods. Nevertheless, there are also many families who are willing to pay a higher price for the prestige of living in one of the new neighborhoods. The worksheet "MidCity" contains data on 128 recent sales in MidCity. For each sale, the sheet shows the neighborhood (1, 2 or 3) in which the house is located, the number of offers made on the house, the square footage, if the house is made primarily of brick or not, the number of bathrooms, the number of bedrooms, and the selling price. Neighborhoods 1 and 2 are more traditional neighborhoods, whereas neighborhood 3 is a newer, more prestigious, neighborhood. a. A realtor wants to understand what factors help explain selling price. As a first pass, create a multiple regression model that can be used to predict selling price from just the quantitative variables (offers, size, bedrooms and bathrooms). Note that the column "Home" is just a label and not part of the regression. What is the estimated regression equation? Use the drop down list below to select your answer. b. Now we want to include the qualitative factors into the model. To do so, use non-brick houses and neighborhood 3 as base cases. That means we will have 3 new (dummy) variables: one for Brick houses, one for Neighborhood 1 and one for Neighborhood 2. Once you have created the 3 dummy variables, develop a new estimated regression equation that has all of the quantitative variables from the previous question as well as the new dummy variables to predict selling price. Use the dropdown below to select the appropriate estimated regression equation. Now suppose that the realtor looked more closely at the data and determined that some of the houses were unusual and not representative of the larger population of houses. She therefore removed some of the houses from the data set and ran a regression with all of the variables--both quantitative and qualitative--in the model. The Regression sheet shows the output from her regression. As in the previous question, the base cases were non-brick houses and those in neighborhood 3. Use the output on the Regression sheet to answer the rest of the parts of question 2. c. What is the predicted (estimated) selling price for a 2400 square foot brick house in neighborhood 2 if it has 4 bedrooms, 3 bathrooms and there are 2 offers made on it? d. All other things being equal, how much do you predict the selling price will change if the number of bedrooms increases by 1? In the choices below, a number in parentheses indicates the selling price will go down, while a positive number indicates the selling price will increase. e. In the previous question, the answer was a point estimate for the coefficient or regression parameter. For this question, provide a 95% confidence interval for the same regression parameter. In other words, estimate the change in selling price for a unit increase in the number of bedrooms with 95% confidence. f. How much of the variation in the sample values of selling price does the estimated regression equation (model) explain? g. Test to see if the overall model is significant (i.e., if any of the independent variables are related to price) at the 0.05 level of significance. Select your answer using the dropdown list in the next cell. h. Is the difference in price between a house in neighborhood 1 and one in neighborhood 3 statistically significant at the 0.05 level of significance? Select your answer using the dropdown list in the next cell. Sales of single-family houses have been brisk in MidCity this year. This has especially been true in older, more established neighborhoods, where housing is relatively inexpensive compared to the new homes being built in the new neighborhoods. Nevertheless, there are also many families who are willing to pay a higher price for the prestige of living in one of the new neighborhoods. The worksheet "MidCity" contains data on 128 recent sales in MidCity. For each sale, the sheet shows the neighborhood (1, 2 or 3) in which the house is located, the number of offers made on the house, the square footage, if the house is made primarily of brick or not, the number of bathrooms, the number of bedrooms, and the selling price. Neighborhoods 1 and 2 are more traditional neighborhoods, whereas neighborhood 3 is a newer, more prestigious, neighborhood. a. A realtor wants to understand what factors help explain selling price. As a first pass, create a multiple regression model that can be used to predict selling price from just the quantitative variables (offers, size, bedrooms and bathrooms). Note that the column "Home" is just a label and not part of the regression. What is the estimated regression equation? Use the drop down list below to select your answer. b. Now we want to include the qualitative factors into the model. To do so, use non-brick houses and neighborhood 3 as base cases. That means we will have 3 new (dummy) variables: one for Brick houses, one for Neighborhood 1 and one for Neighborhood 2. Once you have created the 3 dummy variables, develop a new estimated regression equation that has all of the quantitative variables from the previous question as well as the new dummy variables to predict selling price. Use the dropdown below to select the appropriate estimated regression equation. Now suppose that the realtor looked more closely at the data and determined that some of the houses were unusual and not representative of the larger population of houses. She therefore removed some of the houses from the data set and ran a regression with all of the variables--both quantitative and qualitative--in the model. The Regression sheet shows the output from her regression. As in the previous question, the base cases were non-brick houses and those in neighborhood 3. Use the output on the Regression sheet to answer the rest of the parts of question 2. c. What is the predicted (estimated) selling price for a 2400 square foot brick house in neighborhood 2 if it has 4 bedrooms, 3 bathrooms and there are 2 offers made on it? d. All other things being equal, how much do you predict the selling price will change if the number of bedrooms increases by 1? In the choices below, a number in parentheses indicates the selling price will go down, while a positive number indicates the selling price will increase. e. In the previous question, the answer was a point estimate for the coefficient or regression parameter. For this question, provide a 95% confidence interval for the same regression parameter. In other words, estimate the change in selling price for a unit increase in the number of bedrooms with 95% confidence. f. How much of the variation in the sample values of selling price does the estimated regression equation (model) explain? g. Test to see if the overall model is significant (i.e., if any of the independent variables are related to price) at the 0.05 level of significance. Select your answer using the dropdown list in the next cell. h. Is the difference in price between a house in neighborhood 1 and one in neighborhood 3 statistically significant at the 0.05 level of significance? Select your answer using the dropdown list in the next cell.

Answer rating: 100% (QA)

The number of offers made on the house the square footage whether the house is made primarily of bri... View the full answer