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 500 metric tons of lemon imports. Is the prediction worthwhile? 234 263 537 O Lemon Imports Crash Fatality Rate 16 15.9 496 360 15.7 15.4 15 Find the equation of the regression line. (Round the constant three decimal places as needed. Round the coefficient to six decimal places as needed.) The best predicted crash fatality rate for a year in which there are 500 metric tons of lemon imports is fatalities per 100,000 population. (Round to one decimal place as needed.) Is the prediction worthwhile? O A. Since there appears to be an outlier, the prediction is not appropriate O B. Since common sense suggests there should not be much of a relationship between the two variables, the prediction does not make much sense. OC. Since the sample size is small, the prediction is not appropriate. O D. Since all of the requirements for finding the equation of the regression line are met, the prediction is worthwhile.

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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 500 metric tons of lemon imports. Is the prediction​ worthwhile?

  • Find the equation of the regression line. ^y = __ + __x ​(Round the constant three decimal places as needed. Round the coefficient to six decimal places as​ needed.)
  • The best predicted crash fatality rate for a year in which there are 500 metric tons of lemon imports is ______ fatalities per​ 100,000 population.(Round to one decimal place as​ needed.)
 
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 500 metric tons of lemon imports. Is the prediction worthwhile?
234 263
Lemon Imports
Crash Fatality Rate 16 15.9
360
496
537 D
15.7
15.4
15
Find the equation of the regression line.
(Round the constant three decimal places as needed. Round the coefficient to six decimal places as needed.)
The best predicted crash fatality rate for a year in which there are 500 metric tons of lemon imports is
fatalities per 100,000 population.
(Round to one decimal place as needed.)
Is the prediction worthwhile?
O A. Since there appears to be an outlier, the prediction is not appropriate.
O B. Since common sense suggests there should not be much of a relationship between the two variables, the prediction does not make much sense.
OC. Since the sample size is small, the prediction is not appropriate.
O D. Since all of the requirements for finding the equation of the regression line are met, the prediction is worthwhile.
Transcribed Image Text: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 500 metric tons of lemon imports. Is the prediction worthwhile? 234 263 Lemon Imports Crash Fatality Rate 16 15.9 360 496 537 D 15.7 15.4 15 Find the equation of the regression line. (Round the constant three decimal places as needed. Round the coefficient to six decimal places as needed.) The best predicted crash fatality rate for a year in which there are 500 metric tons of lemon imports is fatalities per 100,000 population. (Round to one decimal place as needed.) Is the prediction worthwhile? O A. Since there appears to be an outlier, the prediction is not appropriate. O B. Since common sense suggests there should not be much of a relationship between the two variables, the prediction does not make much sense. OC. Since the sample size is small, the prediction is not appropriate. O D. Since all of the requirements for finding the equation of the regression line are met, the prediction is worthwhile.
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