A manager of boiler drums wants to use regression analysis to predict the number

Question

A manager of boiler drums wants to use regression analysis to predict the number of worker-hours needed to erect the drums in future projects. Data for 30 randomly selected boilers have been collected. In addition to work hours (Y), the variables measured include boiler capacity, boiler design pressure, boiler type, and drum type. Here is the screenshot of the dataset. Boiler capacity is measured in pounds per hour. Design pressure means in pounds per square inch. Boiler type and Drum recoded into dummy variables. If the boiler type is industrial, it is coded as 1, 0 otherwise. If the Drum type is steam, it is coded as 1, 0 otherwise.type have been

3. Interpret the estimated regression coefficients for each independent variable. (interpret the coefficients into detail)

6. Using the regression output, determine which of the independent variables could be excluded from the regression equation. Justify your choices?

Boiler Worker Hours Boiler Capacity Design Pressure Boiler Type Drum Type Industrial Steam 1,500 Utilit 6,928 3,211 Steam 610,000 610,000 90,000 150,000 88,200 441,000 150,000 441,000 30,000 1,140 Industria Steam 250 Industria Mud 399 Industrial Steam 410 Industria Steam 500 Industrial Mud 410 Industria Mud 325 Industrial Mud 750 Industrial Mud 500 Industrial Mud 3,748 5,651 4,206 1,200 1,965 150,000 627,000 30,000 90,000 150,000 120,000 88,200 441,000 441,000 125,000 150,000 441,000 610,000 88,200 30,000 441,000 1,089,490 65,000 610,000 150,000 6,454 2,048 2,635 4,268 2,974 1,525 Utilit Steam 325 Industrial Steam 1,140 Industrial Mud 500 Industrial Steam 375 Industrial Mud 399 Industrial Steam 410 Industria Mud 410 Industria Mud 750 Industrial Steam 500 Industrial Steam 410 Industria Mud 1,500 Utility Steam 399 Industrial Steam 325 Industrial Mud 410 Industria Steam 2,170 Utility Steam 750 Industrial Steam 4,006 3,775 2,680 3,120 7,606 2,972 1,206 14,791 3,698 1,500 Utility 325 Industrial Steam

Explanation / Answer

3.

Intercept: -4360.13. This means that number of worker hours would remain constant at -4360.13 in absence of all other factors.

Industrial: 3697.64. This means that number of worker hours would go up by 3697.64 for every increase of 1 in industrial, keeping everything else constant.

Steam: 2127.09. This means that number of worker hours would go up by 2127.09 for every increase of 1 in steam, keeping everything else constant.

Boiler Capacity: 0.009. This means that number of worker hours would go up by 0.009 for every increase of 1 in boiler capacity, keeping everything else constant.

Design Pressure: 2.315. This means that number of worker hours would go up by 2.315 for every increase of 1 in design pressure, keeping everything else constant.

6.

Here p-values of all the independent variables are lower than 0.05. Hence, all of them are significant and should not be excluded from the model.

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