A Cost Estimator

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.

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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.

 

Estimation Data

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

 

 

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.

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.

When you complete the linear regression analysis in Excel, please submit the Excel file to myleo.

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