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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.
a. Prepare two scatter plots showing the relationship between the total cost of the projects and each of the two independent variables (X1, and X2). What sort of relationship does each plot suggest? After data analysis, you can find any cell in Excel to write down your interpretation.
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
73604
55
101562
68
3
91055
35
41790
24
75770
12
37420
20
58000
48
97800
36
73960
40
98720
39
4
54190
26
67800
66760
70
88055
68045
104580
93780
30000
53900
90800
180
10
259420
107385
212
6
274755
150
212800
30
74700
0.2
16564
56
92952
0.5
25634
15
67280
160
182055
64
68168
48240
33
45487
18
45140
8
104000
4240
45860
65
48920
57060
80
89150
75
86250
65210
91450
31160
90140
72000
28668
83600
107742
42400
60
57350
58460
102
105680
93000
55495
56875
92460
45800
43
57650
68500
23000
48770
68925
100
105000
28
68530
53
79800
68160
185060
59800
79128
50670
120
154628
210
186500
59760
35600
46425
45200
140
90000
49560
45
56870
96200
42
56550
95420
101545
28153
68520
97822
87230
44
45920
62
75910
86280
250
255455
86480
78
85240
45256
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