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The manager of the main laboratory facility at MetroFit Center is interested in being able to predict the overhead costs each month for the lab.

The manager of the main laboratory facility at MetroFit Center is interested in being able to predict the overhead costs each month for the lab. The manager believes that total overhead varies with the number of lab tests performed but that some costs remain the same each month regardless of the number of lab tests performed. The lab manager collected the following data for the first seven months of the year. (Click the icon to view the data.) The laboratory manager performed a regression analysis to predict total laboratory overhead costs. The output generated by Excel is as follows: | (Click the icon to view the completed regression analysis.) Read the requirements. Requirement 1. Determine the lab's cost equation (use the output from the Excel regression). (Round the amounts to two decimal places.) y = x + Data table n being able to - lat total overhead e same each month The laboratory manager performed a regression analysis to pr overhead costs. The output generated by Excel is as follows: (Click the icon to view the completed regression analysis.) Requirements 1. Determine the lab's cost equation (use the output from the Excel regression). Determine the R-squared (use the output from the Excel regression). 3. Predict the total laboratory overhead for the month if 2,700 tests are performed. Month Number of Lab Tests Performed Total Laboratory Overhead Costs January 2,500 $26,800 February 2,400 $25,700 March ...... 3,200 $25,900 2. April .... 3,650 $28,900 May 3,800 $28,500 June ... 1,800 $20,900 July 1,900 $19,600 Print Done Data table 2 SUMMARY OUTPUT Regression Statistics 3 Multiple R 0.880534 4 R Square 0.77534 5 Adjusted R Square 0.730408 6 Standard Error 1868.82221 7 7 Observations 8 ANOVA 9 10 Regression df SS MS F Significance F 1 60266089.17 60266089.17 17.255877 0.008875 11 Residual 5 17462482.26 3492496.451 12 Total 6 77728571.43 13 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% 14 Intercept 15 X Variable 1 14409.92 2688.513 5.360 0.003 7498.876 21320.960 3.92 0.943 4.154 0.009 1.494 6.343

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