Not sure about my answer here:

To study the factors that affect output in an industry, a random sample of 138 firms who recently made artificial limbs is taken. (Artificial limbs are mechanical replacements for hands and legs.) Researchers ran a regression of number of artificial limbs made in a factory on the number of workers in the factory, the age of the machinery (in years) and the qualification of the workers (in years of study). SUMMARY OUTPUT Regression Statistics Multiple R 0.917475554 R Square 0.841761392 Adjusted R Square 0.838218737 Standard Error 55.80235933 Observations 138 ANOVA of SS MS F Significance F Regression 3 2219660.071 739886.6903 237.6074712 1.9277E-53 Residual 134 417263.0431 3113.903307 Total 137 2636923.114 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Bo: Intercept 219.5397885 13.37425332 16.41510619 7.1016E-34 193.0878458 245.9917312 81: workers 26.600448 1.416523719 18.77868167 2.39594E-39 23.7988109 29.4020851 82: age of machines -29.697 54656 1.721192453 -17.25405343 7.50789E-36 -33. 10176533 -26.29332779 63: years of study 3.621672171 1.901888819 1.904250204 0.059021235 -0. 139932524 7.383276866 Which of the following statements is correct? O Despite the high R squares, it is still possible that there exist some other factors affecting production. Whether to add them into the regression analysis depends on how the adjusted R square would change. O Based the output table, we fail to reject the null hypothesis that all betas are equal to zero at 5% level. This amounts to the fact the regression is not really useful because if does not offer an explanatory power on production. O The regression should have a dummy variable which equals 1 if workers have been to university. However, whether eventually we add this variable in depends how R square would change. O 219.54 artificial limbs are to be expected if a firm has 26.6 workers who have received 3.62 years of study