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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 400 metric tons of lemon imports. Is the prediction worthwhile? Lemon Imports 226 260 356 480 529 Crash Fatality Rate 16.1 15.9 15.6 15.5 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 400 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. B. Since the sample size is small, the prediction is not appropriate. C. Since common sense suggests there should not be much of a relationship between the two variables, the prediction does not make much sense. D. Since all of the requirements for finding the equation of the regression line are met, the prediction is worthwhile.
Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 1.8 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05. Overhead Width (cm) 7.2 7.9 9.6 9.3 8.6 D 9.9 265 Weight (kg) 129 188 226 236 214 Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y=+x. (Round to one decimal place as needed.) The best predicted weight for an overhead width of 1.8 cm is kg. (Round to one decimal place as needed.) Can the prediction be correct? What is wrong with predicting the weight in this case? A. The prediction cannot be correct because a negative weight does not make sense. The width in this case is beyond the scope of the available sample data. B. The prediction cannot be correct because a negative weight does not make sense and because there is not sufficient evidence of a linear correlation. OC. The prediction cannot be correct because there is not sufficient evidence of a linear correlation. The width in this case is beyond the scope of the available sample data. D. The prediction can be correct. There is nothing wrong with predicting the weight in this case. 0 0 0 С