These are all the right answers please show work!!! The red means I marked it wrong previously but it was replaced by the correct answer. I just need to know how it was done so that I can attempt a similar question.

The manager of the main laboratory facility at Capital Health 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. 3: (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=$ 3.74 x+$ 12,412.72 Requirement 2. Determine the R-square (use the output from the Excel regression). The R-square is 0.60721 What does CapitalHealth's R-square indicate? The R-square indicates that the cost equation explains 60.7% of the variability in the data. In other words, it should be used with caution. Capital Health may or may not feel confident using this cost equation to predict total costs at other volumes within the same relevant range. Requirement 3. Predict the total laboratory overhead for the month if 3,200 tests are performed. (Round your answer to the nearest cent.) The total laboratory overhead at a volume of 3,200 lab tests is $ Data Table Number of Lab Tests Performed Total Laboratory Overhead Costs Month January...... 3,250 $27,600 $24,800 February... March..... LLaL LL. . . . . . . . . . . $25,900 April ..... G U LII . . . . . . . . . . . . 3,000 3,750 3,500 4,200 2,200 3,400 May............. $23,500 $27,000 $18,800 June............ July............. $26,500 A Data Table SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square 0.779237 0.60721 0.528651 Standard Error 2067.910396 Observations ANOVA SS MS F Significance F Regression 33053018.68 7.729434 0.038894 1 5 33053018.68 21381267.03 Residual 4276253.406 6 54434285.71 Total Lower Upper Standard Error Coefficients t Stat P-value 95% 95% 12412.72 4548.902 0.041 Intercept X Variable 1 2.729 2.780 719.391 0.282 24106.041 7.204 3.74 1.346 0.039