Data have been collected on the full-time equivalent (FTE) Staffing Level for 34 manufacturing companies and on the annual amount that each company paid out last year in worker compensation payments. A regression model has been built to predict the annual amount paid by a company in worker compensation payments using the staffing level (in FTE) of the company as the independent variable. The output of the regression model from Excel appears below. SUMMARY OUTPUT Regression Statistics Multiple R 0.88 R Square 0.76 Adjusted R Square 0.76 Standard Error 257351.32 Observations 34 ANOVA df 5S MS F Significance F Regression 1 1E+13 18+13 2E+02 8.16128E-16 Residual 32 2E+12 7E+10 Total 33 2E+13 Coefficients Standard Error t Stat -value lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 197878.59 192289.68 -9.15 2.04E-10 -2151414.85 -1368052.34 -2151414.85 -1368052.34 FTE Staffing Level 40166.93 1369.63 14.74 0.075852 5693.60 14372.80 -5693.60 14372.80 Complete the statements below based on your analysis of the regression output shown here. Based on this regression output, we [Select ] reject the null hypothesis at the .05 level of significance that the population coefficient for FTE Staffing Level is equal to 0, and this model appears to explain [ Select ] of the variability present in the sample data for the annual amount that each company paid out last year in worker compensation payments