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 425 metric tons of lemon imports. Is the prediction worthwhile? Use a significance level of 0.05. Lemon Imports 233 270 360 466 511 Crash Fatality Rate 16 15.9 15.5 15.5 15.1 Find the equation of the regression line. y-x (Round the y-intercept to three decimal places as needed. Round the slope to four decimal places as needed.)

Can you please tell also the test statistics and if the weight is close to 518 pounds?

Consider a regression model. The coefficient of determination (R2) gives the proportion of the variability in the dependent variable that is explained by the regression equation.   True False

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 2 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) 7.9 8.4 9.7 7.4 9.6 7.1 Weight (kg) 162 214 263 140 254 155 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 2 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? O 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. O B. The prediction cannot be correct because there is not sufficient evidence of a linear correlation. The…

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 2.1 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)   Weight_(kg)8.4                                   1957.6                                    1919.7                                    2769.5                                     2418.7                                    2288.3                                    216 The regression equation is y=---+----x. ​(Round to one decimal place as​ needed.) The best predicted weight for an overhead width of 2.1 cm is = ___ kg. 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 regression does not appear to be useful for making predictions. B. The…

Listed.below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 1000 mm Hg. Use a significance level of 0.05. Right Arm 102 101 94 79 79 O Left Arm 175 168 146 147 145 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.)

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 2.4 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) 7.4 8.2 9.9 8.5 8.6 8.4 Weight (kg) 139 203 269 184 210 207 A Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y =-220.7 + 49.7 x. (Round to one decimal place as needed.) The best predicted weight for an overhead width of 2.4 cm is - 101.4 kg. (Round to one decimal place as needed.) Can the prediction be correct? What is wrong with predicting the weight in this case? O 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. O B. The prediction cannot be correct because there is not sufficient evidence of a…

Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 85 mm Hg. Use a significance level of 0.05. Right Arm 100 99 93 79 78 P Left Arm 177 170 150 148 148 Click the icon to view the critical values of the Pearson correlation coefficient r |x. (Round to one decimal place as needed.) The regression equation is y = Given that the systolic blood pressure in the right arm is 85 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg. (Round to one decimal place as needed.)

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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 40inches. Is the result close to the actual weight of 352​pounds? Use a significance level of 0.05.a. Chest size​ (inches) 41 54 44 55 39 51   Weight​ (pounds) 328  528  418  580  296  503   a.What is the regression​ equation?  b. The best predicted weight for a bear with a chest size of 39 inches is _______pounds.   ​