A researcher collects data on both lemon imports (x) and the crash fatality rate (y) in the USA. The sevenobservations are randomly selected cities across the country,Crash Fatality Rate1615.8SUMMARY OUTPUT15.615.4Regression StatisticsMultiple RR SquareAdjusted R Square0.93989445715.20.883401590.86008190715Standard Error0.125395887Observations14.8100200300400500600ANOVAdfMSSignificance FRegressionResidual0.595665072 0.595665 37.88223 0.00164668650.078620642 0.015724Total60.674285714Coefficients Standard Error t StatP-valueLower 95% Upper 95% Lower 95.0% Upper 95.0%Intercept16.517435010.18315002 90.18528 3.18E-09 16.04663289 16.988237 16.0466329 16.9882371Lemon Imports-0.0029180880.000474112 -6.15485 0.001647 -0.00413683 -0.0016993 -0.0041368 -0.00169935What is the regression equation?Oy = 16.517x - 0.003Oý = 16.517 - 0.003xOy = 16.517 – 0.003xOý = 16.517x-0.003The best description of the correlation is to describe it asstrong linear correlation.strong negatíve correlation.strong positive correlation.O None of the above4acBook D(1735..(1690...e aaron.W Sign .HB HALE...V Clean.elf My S..Interpret the slope of the line.For a 1 unit increase in lemon imports, the crash fatality rate will, on average, increase by 0.003 units.For a 1 unit increase in lemon imports, the crash fatality rate will, on average, increase by 16.517 units.O For a 1 unit increase in the crash fatality rate, lemon imports will, on average, increase by 0.003 units.OFor a 1 unit increase in the crash fatality rate, lemon imports will, on average, increase by 16.517 units.None of the aboveGive a practical interpretation of the coefficient of determination.O We can predict the crash fatality rate correctly 88.34% of the time using lemon imports in a least-squares regression line.93.99% of the differences in crash fatality rates are caused by differences in lemon imports.O We can predict the crash fatality rate correctly 93.99% of the time using lemon imports in a least-squares regression line.O 93.99% of the sample variation in crash fatality rates can be explained by the least-squares regressionline.88.34% of the differences in crash fatality rates are caused by differences in lemon imports.O 88.34% of the sample variation in crash fatality rates can be explained by the least-squares regressionline.Is it reasonable to use the regression equation to make a prediction for lemon imports of 200? Justify youranswer.O No, the regression line does not fit the points reasonably well.No, r does not indicate that there is a reasonable amount of correlation.No, this prediction is far outside the scope of available data.Yes, all of the criteria are met.What can we say about the relationship between lemon imports and crash fatality rate?Because of the strong relationship, lemon imports are a leading cause of crash fatality rate.The is no relationship between lemon imports and crash fatality rate.While lemon imports and crash fatality rate and strongly related, this relationship is likely due to somethird variable that affects them both.

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What is the regression equation? Oy = 16.517x -0.003 Oŷ = 16.517 – 0.003z %3D Oy = 16.517 - 0.003r Oý = 16.517x - 0.003 The best description of the correlation is to describe it as strong linear correlation. strong negatíve correlation. strong positive correlation. O None of the above

What is the regression equation? Oy = 16.517x -0.003 Oŷ = 16.517 – 0.003z %3D Oy = 16.517 - 0.003r Oý = 16.517x - 0.003 The best description of the correlation is to describe it as strong linear correlation. strong negatíve correlation. strong positive correlation. O None of the above

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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