QUESTIONS (19 MARKS) A manger of a construction company would like to predict ca residential sales in a large city. He has a number of independent variables such as seta (in brandreds of square feet), number of bedrooms, total number of rooms and age. In order to have precise prediction on residential sales, he also includes locatie into consideration such as (l) town (2) imer suburbs and (3) outer suburbs. To represent this five category independent variable (two dummy variables have been created as follows and let location in town be base or reference category Z-10 inner suburbs, otherwise Z-1 outer suburbs. Otherwise A random samples of 30 data collected and a multiple regression analysis is performed. There part of the Malysis displayed in OUTPUT Sd Enter of The Evi Model Summary Adjusted Model RSquare Souare 835 782 Predico, I. AX, Bermek, Totams, 5048 ANOVA M Sum of Su 2957807 Mean Sea r Sig 009 Regression . 454648 19.410 Rei 586.123 23 25484 To 3854010 20 Depen Varattere Predictors. 22. Aeromexa,21 TotalRoom, Coefficients Standardized Coefficients Unstandardized Coufficients @ Sid Error Beta Model Sig - 499673 (Censtart -8.376 12787 BBB .664 252 1.300 194 Araxt Bedrooms: 2 TotalRoomaa 3.620 2971 133 1221 235 8821 2903 616 3.039 008 Age4 804 337 231 1791 Ons 21 .035 3.094 .001 012 991 22 614 3.069 028 200 843 a Dependent Variable Sales Based on OUTPUT, answer the following questions. a) Is the whole model valid? Justify your answer at 5% significant level (4 marka) b) Write down all pomible model obtained in this analysis. (6 marks) e) Predict sales residential when area(XI) is 16, number of bedrooms(X2) # 3, total number of rooms(X3) is 7 and apex) is 15 at all three(3) locaties (6 marks) d) What can you interpret based on (e) wwer? (3 marks) QUESTIONS (19 MARKS) A manger of a construction company would like to predict ca residential sales in a large city. He has a number of independent variables such as seta (in brandreds of square feet), number of bedrooms, total number of rooms and age. In order to have precise prediction on residential sales, he also includes locatie into consideration such as (l) town (2) imer suburbs and (3) outer suburbs. To represent this five category independent variable (two dummy variables have been created as follows and let location in town be base or reference category Z-10 inner suburbs, otherwise Z-1 outer suburbs. Otherwise A random samples of 30 data collected and a multiple regression analysis is performed. There part of the Malysis displayed in OUTPUT Sd Enter of The Evi Model Summary Adjusted Model RSquare Souare 835 782 Predico, I. AX, Bermek, Totams, 5048 ANOVA M Sum of Su 2957807 Mean Sea r Sig 009 Regression . 454648 19.410 Rei 586.123 23 25484 To 3854010 20 Depen Varattere Predictors. 22. Aeromexa,21 TotalRoom, Coefficients Standardized Coefficients Unstandardized Coufficients @ Sid Error Beta Model Sig - 499673 (Censtart -8.376 12787 BBB .664 252 1.300 194 Araxt Bedrooms: 2 TotalRoomaa 3.620 2971 133 1221 235 8821 2903 616 3.039 008 Age4 804 337 231 1791 Ons 21 .035 3.094 .001 012 991 22 614 3.069 028 200 843 a Dependent Variable Sales Based on OUTPUT, answer the following questions. a) Is the whole model valid? Justify your answer at 5% significant level (4 marka) b) Write down all pomible model obtained in this analysis. (6 marks) e) Predict sales residential when area(XI) is 16, number of bedrooms(X2) # 3, total number of rooms(X3) is 7 and apex) is 15 at all three(3) locaties (6 marks) d) What can you interpret based on (e) wwer
