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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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