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 base Click here to view the data. Click here to view the critical values of the correlation coefficient. 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. D. Interpreting the y-intercept is not appropriate. (c) Predict the mean distance of all home runs hit at 105 mph. The mean distance of all home runs hit at 105 mph is feet. (Round to one decimal place as needed.) (d) If a ball was hit with a speed of 105 miles per hour, predict how far it will travel. If a ball is hit with a speed of 105 mph, the distance that it is most likely to travel is feet. (Round to one decimal place as needed.) (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 ball farther than thefeet that would have been predicted given the speed with which the ball was hit. (Round to one decimal place as needed.) () 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. 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. B. 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. O C. Yes, because the least squares regression model is the most accurate way to predict the distance of all home runs hit. 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. O O O O

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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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 correlation coefficient.
画
Data table
O 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.
O D. Interpreting the y-intercept is not appropriate.
Speed (mph)
Distance (feet)
(c) Predict the mean distance of all home runs hit at 105 mph.
101.4
399
The mean distance of all home runs hit at 105 mph is
feet.
103.3
408
(Round to one decimal place as needed.)
101.7
411
103.5
395
(d) If a ball was hit with a speed of 105 miles per hour, predict how far it will travel.
418
105.4
99.3
394
If a ball is hit with a speed of 105 mph, the distance that it is most likely to travel is
(Round to one decimal place as needed.)
feet.
101.3
393
102.1
392
(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.
422
103.5
98.0
395
The ball
farther than the
feet that would have been predicted given the speed with which the ball was hit.
103.4
394
(Round to one decimal place as needed.)
427
110.4
(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. 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.
Print
Done
O B. 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.
O C. Yes, because the least squares regression model is the most accurate way to predict the distance of all home runs hit.
O 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.
O Time Remaining: 02:17:30
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Transcribed Image Text: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 correlation coefficient. 画 Data table O 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. O D. Interpreting the y-intercept is not appropriate. Speed (mph) Distance (feet) (c) Predict the mean distance of all home runs hit at 105 mph. 101.4 399 The mean distance of all home runs hit at 105 mph is feet. 103.3 408 (Round to one decimal place as needed.) 101.7 411 103.5 395 (d) If a ball was hit with a speed of 105 miles per hour, predict how far it will travel. 418 105.4 99.3 394 If a ball is hit with a speed of 105 mph, the distance that it is most likely to travel is (Round to one decimal place as needed.) feet. 101.3 393 102.1 392 (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. 422 103.5 98.0 395 The ball farther than the feet that would have been predicted given the speed with which the ball was hit. 103.4 394 (Round to one decimal place as needed.) 427 110.4 (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. 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. Print Done O B. 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. O C. Yes, because the least squares regression model is the most accurate way to predict the distance of all home runs hit. O 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. O Time Remaining: 02:17:30 Next DEC étv 16 280 MacBook Air DII DD F12 F11 F10 80 F9 F8 F6 F7 F5 F4 esc F2 F3 F1 ( & $ % del @ 4 5 7 2 3 P W E R T Y Q ab K + |/ 00 F.
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 correlation 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=ロ
(Round to three decimal places as needed.)
(b) Interpret the slope and y-intercept, if appropriate.
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.
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.
O 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.
Now interpret the y-intercept.
O A. 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.
O B. 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 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.
O D. Interpreting the y-intercept is not appropriate.
(c) Predict the mean distance of all home runs hit at 105 mph.
The mean distance of all home runs hit at 105 mph is
feet.
O Time Remaining: 02:18:01
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Transcribed Image Text: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 correlation 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=ロ (Round to three decimal places as needed.) (b) Interpret the slope and y-intercept, if appropriate. 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. 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. O 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. Now interpret the y-intercept. O A. 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. O B. 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 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. O D. Interpreting the y-intercept is not appropriate. (c) Predict the mean distance of all home runs hit at 105 mph. The mean distance of all home runs hit at 105 mph is feet. O Time Remaining: 02:18:01 Next DEC étv 16 280 MacBook Air DII DD F12 80 F10 F11 F8 F9 F6 F7 F5 esc F3 F4 F1 F2 & 23 2$ 3 4 5 6 7 8 1 { P Q W E T Y tab F G K // ? ד 2
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