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Determining the total cost of a project

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Reference no: EM133090703

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

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

 

Reference no: EM133090703

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