The table below gives 37 years of data on boats (in thousands)

      The table below gives 37 years of data on boats (in thousands)   registered in Florida and manatees killed by boats. (Data set may be found   here.)   (a) Find the   equation of the least-squares line for predicting manatees killed from   thousands of boats registered. Because the linear pattern is so strong, we   expect predictions from this line to be quite accurate—but only if conditions   in Florida remain similar to those of the past 37 years. (Round your   intercept to two decimal places, and your slope to four decimal places.)   y = ?  + ? x        (b) Suppose   we expect that the number of boats registered in Florida will be 898,000 in   2014. Predict the number of manatees killed by boats in 2014. (Round your   answer to the nearest whole number.)   ? manatees killed        Explain why   we can trust this prediction. (Select all that apply.)   –The   prediction is reliable because of the strong linear association visible in   the scatterplot.   –The   regression line always gives a good prediction.   –The value   of r2 is very close to 1, thus ensuring a high prediction accuracy.   –The   prediction is reliable because the regression line explains 95.3% of the   variations in y.           (c) Predict   manatee deaths if there were no boats registered in Florida. (Round   your answer to two decimal places.)   ? manatees killed        Explain why   the predicted count of deaths is impossible. (We use   x = 0    to find the   intercept of the regression line, but unless the explanatory   variable x actually takes values near 0, prediction for x = 0 is an   example of extrapolation. (Select all that apply.)   –Because x =   0 is far outside the range of values of the explanatory variable.   –Because a   negative number of deaths makes no sense.   –Because   the intercept is usually a non-accurate prediction.  

      Powerboat   registrations (x)  Manatees killed (y)   447 13   460 21   481 24   498 16   513 24   512 20   526 15   559 34   585 33   614 33   645 39   675 43   711 50   719 47      

      The table below gives 37 years of data on boats (in thousands)   registered in Florida and manatees killed by boats. (Data set may be found   here.)   (a) Find the   equation of the least-squares line for predicting manatees killed from   thousands of boats registered. Because the linear pattern is so strong, we   expect predictions from this line to be quite accurate—but only if conditions   in Florida remain similar to those of the past 37 years. (Round your   intercept to two decimal places, and your slope to four decimal places.)   y = ?  + ? x        (b) Suppose   we expect that the number of boats registered in Florida will be 898,000 in   2014. Predict the number of manatees killed by boats in 2014. (Round your   answer to the nearest whole number.)   ? manatees killed        Explain why   we can trust this prediction. (Select all that apply.)   –The   prediction is reliable because of the strong linear association visible in   the scatterplot.   –The   regression line always gives a good prediction.   –The value   of r2 is very close to 1, thus ensuring a high prediction accuracy.   –The   prediction is reliable because the regression line explains 95.3% of the   variations in y.           (c) Predict   manatee deaths if there were no boats registered in Florida. (Round   your answer to two decimal places.)   ? manatees killed        Explain why   the predicted count of deaths is impossible. (We use   x = 0    to find the   intercept of the regression line, but unless the explanatory   variable x actually takes values near 0, prediction for x = 0 is an   example of extrapolation. (Select all that apply.)   –Because x =   0 is far outside the range of values of the explanatory variable.   –Because a   negative number of deaths makes no sense.   –Because   the intercept is usually a non-accurate prediction.  

      Powerboat   registrations (x)  Manatees killed (y)   447 13   460 21   481 24   498 16   513 24   512 20   526 15   559 34   585 33   614 33   645 39   675 43   711 50   719 47