Question: Project 2 A cost estimator for a construction company has collected the data found in the source file Estimation.xlsx describing the total cost (Y) of
Project 2
A cost estimator for a construction company has collected the data found in the source file Estimation.xlsx describing the total cost (Y) of 97 difference projects and the following 3 independent variables thought to exert relevant influence on the total cost: total units of work required (X1), contracted units of work per day (X2), and city/location of work (X3). The cost estimator would like to develop a regression model to predict the total cost of a project as a function of these 3 independent variables.
b. Suppose the estimator wants to use the total units of work required (X1), contracted units of work per day (X2), and city/location of work (X3) as the independent variables to predict total cost. What should be the regression function between Y and X1, X2, and X3? What is the adjusted R-squared value of this model? What conclusions can you make? (Note that X3 is a dummy variable. You should process it into different categories as I showed you in the class lecture. You should expand X3 into Location1, Location 2, .... Location 5 to differentiate the six locations.) After data analysis, you can find any cell in Excel to write down your interpretation.
| Units of Work | Contracted Units per Day | City | Total Cost |
| 50 | 5 | 1 | 83680 |
| 25 | 2 | 1 | 73604 |
| 55 | 1 | 1 | 101562 |
| 68 | 3 | 1 | 91055 |
| 35 | 3 | 1 | 41790 |
| 24 | 2 | 1 | 75770 |
| 12 | 2 | 1 | 37420 |
| 20 | 1 | 1 | 58000 |
| 48 | 2 | 1 | 97800 |
| 36 | 2 | 1 | 73960 |
| 40 | 1 | 1 | 98720 |
| 39 | 4 | 1 | 54190 |
| 26 | 1 | 1 | 67800 |
| 25 | 1 | 1 | 66760 |
| 70 | 3 | 2 | 88055 |
| 36 | 2 | 2 | 68045 |
| 68 | 5 | 2 | 104580 |
| 68 | 2 | 2 | 93780 |
| 12 | 2 | 2 | 30000 |
| 12 | 1 | 2 | 53900 |
| 50 | 2 | 2 | 90800 |
| 180 | 10 | 2 | 259420 |
| 70 | 5 | 2 | 107385 |
| 212 | 6 | 2 | 274755 |
| 150 | 5 | 2 | 212800 |
| 30 | 2 | 2 | 74700 |
| 2 | 0.2 | 2 | 16564 |
| 56 | 4 | 2 | 92952 |
| 10 | 0.5 | 3 | 25634 |
| 15 | 2 | 3 | 67280 |
| 160 | 5 | 3 | 182055 |
| 64 | 5 | 3 | 68168 |
| 35 | 1 | 3 | 48240 |
| 33 | 1 | 3 | 45487 |
| 18 | 4 | 3 | 45140 |
| 150 | 8 | 3 | 104000 |
| 6 | 2 | 3 | 4240 |
| 30 | 1 | 3 | 45860 |
| 65 | 3 | 3 | 48920 |
| 15 | 2 | 3 | 57060 |
| 80 | 2 | 3 | 89150 |
| 75 | 4 | 3 | 86250 |
| 12 | 1 | 3 | 65210 |
| 68 | 3 | 4 | 91450 |
| 10 | 2 | 4 | 31160 |
| 48 | 3 | 4 | 90140 |
| 75 | 4 | 4 | 72000 |
| 10 | 1 | 4 | 28668 |
| 50 | 5 | 4 | 83600 |
| 40 | 1 | 4 | 107742 |
| 36 | 3 | 4 | 42400 |
| 60 | 5 | 4 | 57350 |
| 36 | 2 | 4 | 58460 |
| 102 | 3 | 4 | 105680 |
| 75 | 3 | 4 | 93000 |
| 24 | 0.5 | 4 | 55495 |
| 40 | 2 | 5 | 56875 |
| 75 | 3 | 5 | 92460 |
| 40 | 1 | 5 | 45800 |
| 43 | 5 | 5 | 57650 |
| 40 | 2 | 5 | 68500 |
| 12 | 0.5 | 5 | 23000 |
| 18 | 2 | 5 | 48770 |
| 65 | 2 | 5 | 68925 |
| 100 | 5 | 5 | 105000 |
| 28 | 4 | 5 | 68530 |
| 53 | 2 | 5 | 79800 |
| 100 | 5 | 5 | 68160 |
| 150 | 5 | 5 | 185060 |
| 35 | 3 | 5 | 59800 |
| 80 | 1 | 5 | 79128 |
| 48 | 2 | 5 | 50670 |
| 120 | 3 | 5 | 154628 |
| 210 | 6 | 5 | 186500 |
| 48 | 2 | 5 | 59760 |
| 10 | 5 | 5 | 35600 |
| 20 | 1 | 5 | 46425 |
| 25 | 1 | 5 | 45200 |
| 140 | 3 | 6 | 90000 |
| 25 | 2 | 6 | 49560 |
| 45 | 2 | 6 | 56870 |
| 75 | 3 | 6 | 96200 |
| 42 | 4 | 6 | 56550 |
| 60 | 5 | 6 | 95420 |
| 80 | 2 | 6 | 101545 |
| 12 | 1 | 6 | 28153 |
| 35 | 1 | 6 | 68520 |
| 75 | 3 | 6 | 97822 |
| 68 | 5 | 6 | 86250 |
| 60 | 3 | 6 | 87230 |
| 44 | 1 | 6 | 45920 |
| 62 | 4 | 6 | 75910 |
| 60 | 3 | 6 | 86280 |
| 250 | 2 | 6 | 255455 |
| 50 | 2 | 6 | 86480 |
| 78 | 1 | 6 | 85240 |
| 36 | 3 | 6 | 45256 |
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