When making a prediction with a multiple regression model, what is the difference between the "95% CI" (confidence interval) and the "95% PI" (prediction interval) in Minitab's output? The CI is for the population and the PI is for the sample. The CI is for a population mean and the PI is for a single observation. The CI assumes constant variance and the PI does not. The CI and the PI are the same. The CI is for the model's intercept and the PI is for the model's slope.

QUESTION 1 Match each confidence interval to its interpretation. 95% Cl for u: (-0.7, -0.4) A. We are 95% confident that the mean price increases by 0.4 to 0.7 every year. 95% CI for p: (0.4, 0.7) B. We are 95% confident that the population standard deviation is 4 95% CI for p1 - P2: (0.4, 0.7) between 0.4 and 0.7. $ 95% CI for o: (0.4, 0.7) C. We are 95% confident that the mean difference for the population is between -0.7 and -0.4. 4 95% CI for Hd: (-0.7, -0.4) D. We are 95% confident that, for a given size, the mean price $ 95% CI for p (rho): (-0.7, -0.4) decreases by 0.4 to 0.7 every year. E. We are 95% confident that the population proportion is between With price =2.25+ 0.55(year) 0.4 and 0.7. 95% CI for Byear: (0.4, 0.7) F. We are 95% confident that the proportion for population 1 is between 0.4 and 0.7 more than the proportion for population 2. With price =7.25- 0.55(year) + 5.00(size ) G. We are 95% confifdent that the population average is between 95% CI for Byear: (-0.4, -0.7) -0.7 and -0.4. H. We are 95% confident that the true correlation is between -0.7 and -0.4