Find the regression equation, letting the first variable be the predictor (x) variable. Using the listed lemon/crash data, where lemon imports are in metric tons and the fatality rates are per 100,000 people, find the best predicted crash fatality rate for a year in which there are 475 metric tons of lemon imports. Is the prediction worthwhile? Use a significance level of 0.05. Lemon Imports 235 264 356 Crash Fatality Rate 16 15.9 15.6 476 518 15.3 D 15.1 Find the equation of the regression line. + (Round the y-intercept to three decimal places as needed. Round the slope to four decimal places as needed.) The best predicted crash fatality rate for a year in which there are 475 metric tons of lemon imports is fatalities per 100,000 population. (Round to one decimal place as needed.) Is the prediction worthwhile? OA. Since there appears to be an outlier, the prediction is not appropriate. OB. Since all of the requirements for finding the equation of the regression line are met, the prediction is worthwhile. OC. Since common sense suggests there should not be much of a relationship between the two variables, the prediction does not make much sense. OD. Since the sample size is small, the prediction is not appropriate. The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 48 inches. Is the result close to the actual weight of 498 pounds? Use a significance level of 0.05. Chest size (inches) Weight (pounds) 36 56 50 39 49 42 296 570 501 353 487 385 Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? y=+x (Round to one decimal place as needed.) X What is the best predicted weight of a bear with a chest size of 48 inches? The best predicted weight for a bear with a chest size of 48 inches is (Round to one decimal place as needed.) pounds. Is the result close to the actual weight of 498 pounds? OA. This result is close to the actual weight of the bear. OB. This result is very close to the actual weight of the bear. OC. This result is exactly the same as the actual weight of the bear. OD. This result is not very close to the actual weight of the bear.