The table below shows the numbers of new-vehicle sales (in thousands) for Company 1 and Company 2 for 11 years. Construct and interpret a 99% prediction interval for new-vehicle sales for Company 2 when the number of new vehicles sold by Company 1 is 2766 thousand. The equation of the regression line is y = 1.208x+410.022. Company 1, x Company 2, y 4074 3966 3637 3416 3292 3083 2860 2474 1995 4932 4843 4799 4750 4666 4459 4050 3841 2965 1616 1948 2054 2234 Construct and interpret a 99% prediction interval for new-vehicle sales for Company 2 when the number of new vehicles sold by Company 1 is 2766 thousand. Select the correct choice below and fill in the answer boxes to complete your choice. (Round to the nearest cent as needed.) A. We can be 99% confident that when the new-vehicle sales for Company 1 is 2766 thousand, the new-vehicle sales for Company 2 will be between and thousand. B. There is a 99% chance that the predicted new-vehicle sales for Company 2 is between and thousand, given that the new-vehicle sales for Company 1 is 2766 thousand.

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

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?

Does Table 1 represent a linear function? If so, finda linear equation that models the data.