# 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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