SUMMARY OUTPUT Regression Stotis tics Multiple R 0.8908769 R Square 0.793661651 Adjusted R Squa re 0.742077063 Standard Error 1.489341215 Observations 6 ANOVA o'f 55 MS F Significance F Regression 1 34.12745098 34.12745098 15.38563536 0.017212066 Residual 4 8.87254902 2.218137255 Total 5 43 Coefficients Standard Error t Stat P-volue Lower 95% Upper 95% Intercept 2.294117647 1.459846389 1.571478797 0.191168664 -1.759065715 6.347301009 Payroll {$100k} 1.156862745 0.294933509 3.922452722 0.017212066 0.337996048 1.975729442 Questions to interpret the results of the regression analysis Type answer in the box 1. Is the regression model statistically significant {Yes or No)? 2. Highlight the cell in green that corresponds to the statistic value you used to answer question #1. 3. For each unit increase in Payroll, how much do Sales change? 4. How much variability {in %) in Sales, can be explained by the regression model with Payroll as the independent variable? 5. Highlight the cell in blue that corresponds to the statistic value you used to answer question #4. 6. What is the proiected Sales {in $100k] when the Payrpll is $0? Type answer in Questions to interpret the results ofthe regression analysis the box 1. Is the regression model statistically significant {Yes or No]? 2. Highlight the cell in green that corresponds to the statistic value you used to answer question #1. 3. For each unit increase in Starter Replacements, how much do Flight Hours change? 4. How much variability {in %) in Flight Hours, can be explained by the regression model with Starter Replacements as the independent variable? 5. Highlight the cell in blue that corresponds to the statistic value you used to answer question #4. 6. What is the projected Flight Hours {1000 hrs] when the Starter Replacements is O? 7. Highlight the cell in yellow that correponds to the Correlation Coefficient. 8. If the Starter Replacements is 13, what are the estimated Flight Hours (1000 Hrs]