Please do all requirments thank you! The manager of the main laboratory facility
ID: 2394433 • Letter: P
Question
Please do all requirments thank you!
The manager of the main laboratory facility at CapitalHealth After running a regression analysis on the first seven months Center is interested in being able to predict the overheadof the year, the manager of the main laboratory facility at costs each month for the lab. The manager believes that total Capital Health Center collects seven additional months of overhead varies with the number of lab tests performed but data. The number of tests performed and the total monthly that some costs remain the same each month regardless of overhead costs for the lab follow the number of lab tests performed (Click the icon to view the data.) Read the requirements Requirements 1 and 2. Use Excel to run a regression analysis using data for August through February and determine the lab's cost equation (use the output from the regression analysis you perform using Excel) (Enter all dollar amounts to two decimal places.)Explanation / Answer
Month Number of lab tests Performed Total Laboratory Overhead August 3,600 $ 26,780 September 3,750 $ 24,350 October 4,050 $ 26,050 November 2,600 $ 23,450 December 4,200 $ 27,850 January 2,900 $ 19,540 February 3,950 $ 25,500 SUMMARY OUTPUT Regression Statistics Multiple R 0.765131285 R Square 0.585425883 Adjusted R Square 0.50251106 Standard Error 1930.916355 Observations 7 ANOVA df SS MS F Significance F Regression 1 26324895.87 26324896 7.060569625 0.045036546 Residual 5 18642189.84 3728438 Total 6 44967085.71 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 12393.57422 4721.477012 2.624936 0.0468182 256.6311731 24530.51726 256.6311731 24530.51726 X Variable 1 3.463671875 1.303517521 2.657173 0.045036546 0.112873412 6.814470338 0.112873412 6.814470338 1 Open excel put the data and go to data - data analysis- Regression Set Y and X range, output will be as given above. 2 Y = $3.46X + $12393.57 3 R square = 0.5854 R square indicates that how this model indiactes all of the varaition around the mean, higher the R square better its fitted. Its explains only 59% variations 4 Y = 3.46 x 3500 + 12393.57 Y = $24,503.57