FloorArea (Sq.Ft.) Offices Entrances Age AssessedValue ($'000) 4790 4 2 8 1796 4720 3 2 12 1544 5940 4 2 2 2094 5720 4 2 34 1968 3660 3 2 38 1567 5000 4 2 31 1878 2990 2 1 19 949 2610 2 1 48 910 5650 4 2 42 1774 3570 2 1 4 1187 2930 3 2 15 1113 1280 2 1 31 671 4880 3 2 42 1678 1620 1 2 35 710 1820 2 1 17 678 4530 2 2 5 1585 2570 2 1 13 842 4690 2 2 45 1539 1280 1 1 45 433 4100 3 1 27 1268 3530 2 2 41 1251 3660 2 2 33 1094 1110 1 2 50 638 2670 2 2 39 999 1100 1 1 20 653 5810 4 3 17 1914 2560 2 2 24 772 2340 3 1 5 890 3690 2 2 15 1282 3580 3 2 27 1264 3610 2 1 8 1162 3960 3 2 17 1447 Construct a multiple regression model. Use Excel’s Analysis ToolPak to conduct a regression analysis with AssessmentValue as the dependent variable and FloorArea, Offices, Entrances, and Age as independent variables. What is the overall fit r^2? What is the adjusted r^2? Which predictors are considered significant if we work with α=0.05? Which predictors can be eliminated? What is the final model if we only use FloorArea and Offices as predictors? Suppose our final model is: AssessedValue = 115.9 + 0.26 x FloorArea + 78.34 x Offices What wouldbe the assessed value of a medical office building with a floor area of 3500 sq. ft., 2 offices, that was built 15 years ago? Is this assessed value consistent with what appears in the database?
FloorArea (Sq.Ft.) Offices Entrances Age AssessedValue ($'000) 4790 4 2 8 1796 4720 3 2 12 1544 5940 4 2 2 2094 5720 4 2 34 1968 3660 3 2 38 1567 5000 4 2 31 1878 2990 2 1 19 949 2610 2 1 48 910 5650 4 2 42 1774 3570 2 1 4 1187 2930 3 2 15 1113 1280 2 1 31 671 4880 3 2 42 1678 1620 1 2 35 710 1820 2 1 17 678 4530 2 2 5 1585 2570 2 1 13 842 4690 2 2 45 1539 1280 1 1 45 433 4100 3 1 27 1268 3530 2 2 41 1251 3660 2 2 33 1094 1110 1 2 50 638 2670 2 2 39 999 1100 1 1 20 653 5810 4 3 17 1914 2560 2 2 24 772 2340 3 1 5 890 3690 2 2 15 1282 3580 3 2 27 1264 3610 2 1 8 1162 3960 3 2 17 1447 Construct a multiple regression model. Use Excel’s Analysis ToolPak to conduct a regression analysis with AssessmentValue as the dependent variable and FloorArea, Offices, Entrances, and Age as independent variables. What is the overall fit r^2? What is the adjusted r^2? Which predictors are considered significant if we work with α=0.05? Which predictors can be eliminated? What is the final model if we only use FloorArea and Offices as predictors? Suppose our final model is: AssessedValue = 115.9 + 0.26 x FloorArea + 78.34 x Offices What wouldbe the assessed value of a medical office building with a floor area of 3500 sq. ft., 2 offices, that was built 15 years ago? Is this assessed value consistent with what appears in the database?
Trigonometry (MindTap Course List)
8th Edition
ISBN:9781305652224
Author:Charles P. McKeague, Mark D. Turner
Publisher:Charles P. McKeague, Mark D. Turner
Chapter4: Graphing And Inverse Functions
Section: Chapter Questions
Problem 6GP: If your graphing calculator is capable of computing a least-squares sinusoidal regression model, use...
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| FloorArea (Sq.Ft.) | Offices | Entrances | Age | AssessedValue ($'000) |
| 4790 | 4 | 2 | 8 | 1796 |
| 4720 | 3 | 2 | 12 | 1544 |
| 5940 | 4 | 2 | 2 | 2094 |
| 5720 | 4 | 2 | 34 | 1968 |
| 3660 | 3 | 2 | 38 | 1567 |
| 5000 | 4 | 2 | 31 | 1878 |
| 2990 | 2 | 1 | 19 | 949 |
| 2610 | 2 | 1 | 48 | 910 |
| 5650 | 4 | 2 | 42 | 1774 |
| 3570 | 2 | 1 | 4 | 1187 |
| 2930 | 3 | 2 | 15 | 1113 |
| 1280 | 2 | 1 | 31 | 671 |
| 4880 | 3 | 2 | 42 | 1678 |
| 1620 | 1 | 2 | 35 | 710 |
| 1820 | 2 | 1 | 17 | 678 |
| 4530 | 2 | 2 | 5 | 1585 |
| 2570 | 2 | 1 | 13 | 842 |
| 4690 | 2 | 2 | 45 | 1539 |
| 1280 | 1 | 1 | 45 | 433 |
| 4100 | 3 | 1 | 27 | 1268 |
| 3530 | 2 | 2 | 41 | 1251 |
| 3660 | 2 | 2 | 33 | 1094 |
| 1110 | 1 | 2 | 50 | 638 |
| 2670 | 2 | 2 | 39 | 999 |
| 1100 | 1 | 1 | 20 | 653 |
| 5810 | 4 | 3 | 17 | 1914 |
| 2560 | 2 | 2 | 24 | 772 |
| 2340 | 3 | 1 | 5 | 890 |
| 3690 | 2 | 2 | 15 | 1282 |
| 3580 | 3 | 2 | 27 | 1264 |
| 3610 | 2 | 1 | 8 | 1162 |
| 3960 | 3 | 2 | 17 | 1447 |
Construct a multiple regression model.
- Use Excel’s Analysis ToolPak to conduct a
regression analysis with AssessmentValue as the dependent variable and FloorArea, Offices, Entrances, and Age as independent variables. What is the overall fit r^2? What is the adjusted r^2? - Which predictors are considered significant if we work with α=0.05? Which predictors can be eliminated?
- What is the final model if we only use FloorArea and Offices as predictors?
- Suppose our final model is:
- AssessedValue = 115.9 + 0.26 x FloorArea + 78.34 x Offices
- What wouldbe the assessed value of a medical office building with a floor area of 3500 sq. ft., 2 offices, that was built 15 years ago? Is this assessed value consistent with what appears in the database?
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