12_Multiple_Regression_Part2 - MULTIPLE REGRESSION PART 2 Topics Outline Running Multiple Regression and Interpreting the Results Validation of the Fit

# 12_Multiple_Regression_Part2 - MULTIPLE REGRESSION PART 2...

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- 1 - MULTIPLE REGRESSION – PART 2 Topics Outline Running Multiple Regression and Interpreting the Results Validation of the Fit Running Multiple Regression and Interpreting the Results Example 1 Overhead Costs at Bendrix The Bendrix Company manufactures various types of parts for automobiles. The manager of the factory wants to get a better understanding of overhead costs and has tracked total overhead costs for the past 36 months. To help explain these, he has also collected data on two variables that are related to the amount of work done at the factory (see Overhead_Costs.xlsx): – MachHrs: number of machine hours used during the month – ProdRuns: the number of separate production runs during the month Our earlier analysis of the two candidates for explanatory variables (see Overhead_Costs_Finished.xlsxindicated that both variables are related to Overhead. Therefore, it makes sense to try including both in the regression equation. With any luck, the linear fit should improve. (a) Use StatTools and Overhead_Costs.xlsxto run a regression for Overhead costs as a linear function of MachHrs (machine hours) and ProdRuns (production runs). (Use Overhead_Costs_MultipleRegression_Finished.xlsx as a reference.)To obtain the regression output, select Regression from the StatTools Regression and Classification dropdown list and fill out the resulting dialog box as shown below. )
- 2 - 50.839 (b) What is the equation of the regression model? (c) What is the equation of the true regression surface? . (d) What is the equation of the fitted surface? .
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