The following Excel file contains data on selling price ("Price" in thousands), age at lime of sale ("Age"), and total square footage ("SF") for houses in a midwestem city. Click the the following link to open the flle: HOUSE 3.x15x An analyst wants to predict the sale price of a home using its total square footage. She chooses to estimate the following regression model: Price=0+1Age+2SF+ Fit the model to the data in the spreadsheet using the Data Analysis Toolpak (instructions below) and then answer the following questions. Take all calculations and inputs to two (2) decimal places. For blanks followed by two words or phrases in parentheses separated by a slash (h), enter one of the words or phrases in parentheses for that blank. For questions involving variable names, use the variable names given in the paragraph above (given in bold) instead of just "X" and "Y." 1. The Model F test statistic is At the a=0.05 level of significance, the analyst would (rejectfall to reject) the null hypothesis. The analyst (can/cannot) conclude that at least one of the predictor variables, age and square footoge, are potentially useful for predicting price. 2. The estimated regression equation is (Ag0)+(SF) 3. Adjusted R-Squared is 4. The analyst can be 95% confident that the increase in the mean selling price for a one-year increase in age is between and , holding fixed. 5. The test statistic for testing the hypotheses H0:2=0H3:3=0 5. The test statistic for testing the hypotheses H0:2=0H12=0 is At the a=0.05 level of significance, the analyst would therefore (rejectlail to reject) the null hypothesis. The analyst (can/cannot) conclude that the variable (age/SF) is potentially useful for predicting price when the variable (age/SF) is in the model as well. 6. The estimated mean price (in thousands) for a house that is 9 years old with 2325 square feet is $ Fo enable the Data Analysis Toolpak in Excel, go to Data Data Analysis Regression). The following Excel file contains data on selling price ("Price" in thousands), age at lime of sale ("Age"), and total square footage ("SF") for houses in a midwestem city. Click the the following link to open the flle: HOUSE 3.x15x An analyst wants to predict the sale price of a home using its total square footage. She chooses to estimate the following regression model: Price=0+1Age+2SF+ Fit the model to the data in the spreadsheet using the Data Analysis Toolpak (instructions below) and then answer the following questions. Take all calculations and inputs to two (2) decimal places. For blanks followed by two words or phrases in parentheses separated by a slash (h), enter one of the words or phrases in parentheses for that blank. For questions involving variable names, use the variable names given in the paragraph above (given in bold) instead of just "X" and "Y." 1. The Model F test statistic is At the a=0.05 level of significance, the analyst would (rejectfall to reject) the null hypothesis. The analyst (can/cannot) conclude that at least one of the predictor variables, age and square footoge, are potentially useful for predicting price. 2. The estimated regression equation is (Ag0)+(SF) 3. Adjusted R-Squared is 4. The analyst can be 95% confident that the increase in the mean selling price for a one-year increase in age is between and , holding fixed. 5. The test statistic for testing the hypotheses H0:2=0H3:3=0 5. The test statistic for testing the hypotheses H0:2=0H12=0 is At the a=0.05 level of significance, the analyst would therefore (rejectlail to reject) the null hypothesis. The analyst (can/cannot) conclude that the variable (age/SF) is potentially useful for predicting price when the variable (age/SF) is in the model as well. 6. The estimated mean price (in thousands) for a house that is 9 years old with 2325 square feet is $ Fo enable the Data Analysis Toolpak in Excel, go to Data Data Analysis Regression)
