The following data represent the speed at which a ball was hit (in miles per hour) and the distance it traveled (in feet) for a random sample of home runs in a Major League baseball game in 2018. Complete parts (a) through (f).Click here to view the data.Click here to view the critical values of the corelation coefficient.... ..(b) Interpret the slope and y-intercept, if appropriate.Critical values for the correlation coefficientBegin by interpreting the slope.O A. The slope of this least-squares regression line says that the distance the ball travels increases by the slope with every 1 mile per hour increase in the speed that the ball was hit.O B. The slope of this least-squares regression line shows the increase in the speed that the ball was hit with every 1 foot increase in the distance that the ball was hit.Critical Values for Correlation CoefficientO C. The slope of this least-squares regression line shows the distance that the ball would travel when the speed that the ball is hit is zero.O D. Interpreting the slope is not appropriate.0.99740.950Now interpret the y-intercept.50.8780.811O A. The y-intercept of this least-squares regression line shows the speed that the ball is hit at when the distance that the ball travels is zero.0.754O B. The y-intercept of this least-squares regression line shows the distance that the ball would travel when the speed that the ball is hit is zero.0.70780.666O C. The y-intercept of this least-squares regression line shows the increase in the speed that the ball was hit with every 1 foot increase in the distance that the ball was hit.100.632O D. Interpreting the y-intercept is not appropriate.110.602120.576(c) Predict the mean distance of all home runs hit at 107 mph.130.553The mean distance of all home runs hit at 107 mph is(Round to one decimal place as needed.)feet.140.532150.514160.497(d) If a ball was hit with a speed of 107 milles per hour, predict how far it will travel.170.482If a ball is hit with a speed of 107 mph, the distance that it is most likely to travel is(Round to one decimal place as needed.)180.468feet.190.456200.444(e) Christian Yelich hit a home run 398 feet. The speed at which the ball was hit was 106.2 mph. Did this ball travel farther than you would have predicted? Explain.210.433220.423The ballfarther than thefeet that would have been predicted given the speed with which the ball was hit230.413(Round to one decimal place as needed.)240.404(f) Would you feel comfortable using the least-squares regression model on home runs where the speed of the ball was 122 mph? Explain.250.396260.388O A. Yes, because the least squares regression model can accurately predict the distance of home runs with a higher speed than was observed, but not lower.270.381O B. Yes, because the least squares regression model is the most accurate way to predict the distance of all home runs hit280.374290.367O C. No, because the least squares regression model cannot predict the distance of a home run when the speed of the ball is outside of the scope of the model.300.361O D. No, because the least squares regression model can accurately predict the distance of home runs with a lower speed than was observed, but not higher.PrintDoneO TimeDETCOA2334403u 10-8-2014MAINTCOA2330401 The following data represent the speed at which a ball was hit (in miles per hour) and the distance it traveled (in feet) for a random sample of home runs in a Major League baseball game in 2018. Complete parts (a) through (f).Click here to view the data.Click here to view the critical values of the corelation coefficient(a) Find the least-squares regression line treating speed at which the ball was hit as the explanatory variable and distance the ball traveled as the response variable.y(Round to three decimal places as needed.)(b) Interpret the slope and y-intercept, if appropriate.Begin by interpreting the slope.Data tableO A. The slope of this least-squares regression line says that the distance the ball travels increases by the slope with every 1 mile per hour increase in the speed that the ball was hit.O B. The slope of this least-squares regression line shows the increase in the speed that the ball was hit with every 1 foot increase in the distance that the ball was hit.Speed (mph)Distance (feet) OO C. The slope of this least-squares regression line shows the distance that the ball would travel when the speed that the ball is hit is zero.110.4427O D. Interpreting the slope is not appropriate.105.5414101.4399Now interpret the y-intercept.100.7396103.5422O A. The y-intercept of this least-squares regression line shows the speed that the ball is hit at when the distance that the ball travels is zero.101.7411O B. The y-intercept of this least-squares regression line shows the distance that the ball would travel when the speed that the ball is hit is zero.103.6402O C. The y-intercept of this least-squares regression line shows the increase in the speed that the ball was hit with every 1 foot increase in the distance that the ball was hit.99.3394O D. Interpreting the y-intercept is not appropriate.100.3392102.1392(c) Predict the mean distance of all home runs hit at 107 mph.5.4418101.2392The mean distance of all home runs hit at 107 mph is(Round to one decimal place as needed.)feet.(d) If a ball was hit with a speed of 107 miles per hour, predict how far it will travel.PrintDoneIf a ball is hit with a speed of 107 mph, the distance that it is most likely to travel is(Round to one decimal place as needed.)feet.(e) Christian Yelich hit a home run 398 feet. The speed at which the ball was hit was 106.2 mph. Did this ball travel farther than you would have predicted? Explain.The ballfarther than thefeet that would have been predicted given the speed with which the ball was hit(Round to one decimal place as needed.)(f) Would you feel comfortable using the least-squares regression model on home runs where the speed of the ball was 122 mph? Explain.O A. Yes, because the least squares regression model can accurately predict the distance of home runs with a higher speed than was observed, but not lower.DOTCOA2334403U 10-8-2014MAINTCOA2330401

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Click here to view the data. Click here to view the critical values of the correlation coefficient ..... (b) Interpret the slope and y-intercept, if appropriate. Critical values fo Begin by interpreting the slope. O A. The slope of this least-squares regression line says that the distance the ball travels increases by the slope with every 1 mile per hour increase in the speed that the ball was hit. O B. The slope of this least-squares regression line shows the increase in the speed that the ball was hit with every 1 foot increase in the distance that the ball was hit OC. The slope of this least-squares regression line shows the distance that the ball would travel when the speed that the ball s hit is OD. Interpreting the slope is not appropriate. Critical Values f 3. 4. Now interpret the y-intercept. 6. O A. The y-Intercept of this least-squares regression line shows the speed that the ball is hit at when the distance that the ball travels is zero. O B. The y-intercept of this least-squares regression line shows the distance that the ball would travel when the speed that the ball is hit is zero. OC. The y-Intercept of this least-squares regression line shows the increase in the speed that the ball was hit with every 1 foot increase in the distance that the ball was hit O D. Interpreting the y-Intercept is not appropriate 9 10 12 (c) Predict the mean distance of all home runs hit at 107 mph 13 The mean distance of all home runs hit at 107 mph is feet. (Round to one decimal place as needed.) 14 15 16 (d) If a ball was hit with a speed of 107 miles per hour, predict how far it will travel 17 If a bal is hit with a speed of 107 mph, the distance that it is most likely to travel is feet 18

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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