Summary Output >Dependent Variable: Number of Chairs Produced Regression Statistics Multiple R 0.8402 R Square 0.7059 Adjusted R Square 0.6958 Standard Error 26.03 Observations 61 ANOVA df SS MS F Significance F Regression 2 9.43E+4 4.72E+04 69.61 0.0000 Residual 58 3.93E+04 6.78E+02 Total 60 1.34E+05 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 405.58 4.53 89.51 0.0000 396.51 414.65 Weekday (1=weekday, 0=weekend) 70.97 7.58 9.36 0.0000 55.79 86.15 Shift Time (1=morning, 0=evening) 47.85 7.02 6.82 0.0000 33.80 61.91 Q-1 Below is some output from the regression on the furniture factory data. What does the R-square value tell us? A.0 That we cannot reject the null hypothesis B.0 That there is multicollinearity between the independent variables C.0 That on average, 0.7059 more chairs are produced during weekday shifts than during weekend shifts. D.0 That 71% of the variability in the number of chairs produced can be explained by whether the shift is in the morning or evening and whether it is a weekday shift or weekend shift