The following is a scatterplot of the liters of alcohol from drinking wine per person and the death rates from heart disease per 100,000 people for each of 19 countries. The least-squares regression line has been drawn in on the plot. Death Rate from Heart Disease 300 250 200 150 100 50 0 3 4 5 6 7 Alcohol from Drinking Wine Based on the least-squares regression line, we would predict that in a country where (per person) 2 liters of alcohol from wine is consumed, the death rate from heart disease per 100,000 people would be about: O a. 50. O b. 100. c. 225 O d. 150

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?