Why is Adjusted R Squared preferred to R Squared to assess the fit of a regression model? Because R Squared always increases when variables are added to the model Because R Squared substantially underestimates the variability of the dependent variable accounted for by the independent variables Because R Squared does not account for the possibility of the non-normal distribution of the dependent variable. Because R Squared measures a less relevant type of model fit than adjusted R Squared.

Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?

Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4

What is regression analysis? Describe the process of performing regression analysis on a graphing utility.