Pls help I need in one hour thankyou 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 ratefor a year in which there are 425 metric tons of lemon imports. Is the prediction worthwhile?Lemon ImportsCrash Fatality Rate 16 15.7232 265351462538 D15.415.414.9Find the equation of the regression line.y-ロ+O×(Round the y-intercept to three decimal places as needed. Round the slope to four decimal places as needed.))xThe best predicted crash fatality rate for a year in which there are 425 metric tons of lemon imports is(Round to one decimal place as needed.)fatalities per 100,000 population.Is the prediction worthwhile?O A. Since the sample size is small, the prediction is not appropriate.O B. Since all of the requirements for finding the equation of the regression line are met, the prediction is worthwhile.O C. Since there appears to be an outlier, the prediction is not appropriate.O D. Since common sense suggests there should not be much of a relationship between the two variables, the prediction does not make much sense.

Solution To find regression equation use the following steps in excel.

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 yoar in which there are 425 metric tons of lemon imports. Is the prediction worthwhile? Lemon Imports 232 265 Crash Fatality Rate 16 15.7 15.4 351 462 15,4 14.9 538 D Find the equation of the regression line. (Round the y-intercept 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 425 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 the sample size is small, the prediction is not appropriate. O B. Since all of the requirements for finding the equation of the regression line are met, the prediction is worthwhile. OC. Since there appears to be an outlier, the prediction is not appropriate.

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 yoar in which there are 425 metric tons of lemon imports. Is the prediction worthwhile? Lemon Imports 232 265 Crash Fatality Rate 16 15.7 15.4 351 462 15,4 14.9 538 D Find the equation of the regression line. (Round the y-intercept 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 425 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 the sample size is small, the prediction is not appropriate. O B. Since all of the requirements for finding the equation of the regression line are met, the prediction is worthwhile. OC. Since there appears to be an outlier, the prediction is not appropriate.

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)

6th Edition

ISBN:9781337111348

Author:Bruce Crauder, Benny Evans, Alan Noell

Publisher:Bruce Crauder, Benny Evans, Alan Noell

Chapter3: Straight Lines And Linear Functions

Section3.CR: Chapter Review Exercises

Problem 15CR: Life Expectancy The following table shows the average life expectancy, in years, of a child born in...

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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?
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