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. (Round to three decimal places as needed.) (b) Interpret the slope and y-intercept, if appropriate. Begin by interpreting the slope. - X Data table 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. Speed (mph) Distance (feet) o 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. 110.4 427 O D. Interpreting the slope is not appropriate. 105.5 414 101.4 399 Now interpret the y-intercept 100.7 396 103.5 422 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. 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 101.7 411 103.6 402 99.3 394 100.3 392 102.1 392 (c) Predict the mean distance of al home runs hit at 107 mph, 105.4 418 101.2 392 The mean distance of all home runs hit at 107 mph is feet (Round to one decimal place as needed)

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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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.
(Round to three decimal places as needed.)
(b) Interpret the slope and y-intercept, if appropriate.
Begin by interpreting the slope.
Data table
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.
Speed (mph) Distance (feet) O
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.
110.4
427
105.5
414
101.4
399
Now interpret the y-intercept.
100.7
396
103.5
422
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
101.7
411
103.6
402
99.3
394
100,3
392
102.1
392
(c) Predict the mean distance of all home rus hit at 107 mph,
105.4
418
101.2
392
The mean distance of all home runs hit at 107 mph is feet
(Round to one decimal place as needed.)
(d) If a ball was hit with a speed of 107 miles per hour, predict how far it will travel.
Print
Done
If a ball is hit with a speed of 107 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
V farther than the feet that would have been predicted given the speed with which the ball was hit.
(Round to one decimal place as needed)
(1) 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.
TA
TCOA2334403
LI 10&-201
MAIN
TOBAZ330AD
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. (Round to three decimal places as needed.) (b) Interpret the slope and y-intercept, if appropriate. Begin by interpreting the slope. Data table 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. Speed (mph) Distance (feet) O 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. 110.4 427 105.5 414 101.4 399 Now interpret the y-intercept. 100.7 396 103.5 422 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 101.7 411 103.6 402 99.3 394 100,3 392 102.1 392 (c) Predict the mean distance of all home rus hit at 107 mph, 105.4 418 101.2 392 The mean distance of all home runs hit at 107 mph is feet (Round to one decimal place as needed.) (d) If a ball was hit with a speed of 107 miles per hour, predict how far it will travel. Print Done If a ball is hit with a speed of 107 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 V farther than the feet that would have been predicted given the speed with which the ball was hit. (Round to one decimal place as needed) (1) 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. TA TCOA2334403 LI 10&-201 MAIN TOBAZ330AD
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 basebal game in 2018. Complete parts (a) through (1).
Click here to view the data,
Click here to view the critical.valu0s of the correlation coefficient
.***.
(b) Interpret the slope and y-Intercept, if appropriate.
Critical values for the correlation coefficient
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
Critical Values for Correlation Coefficient
OC. 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.
3.
0.997
4
0.950
Now interpret the y-intercept.
0878
6.
0.811
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,
7
0.754
8
0.707
9
0.666
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
10
0.632
O D. Interpreting the y-Intercept is not appropriate
0.602
12
0.576
(c) Predict the mean distance of all home runs hit at 107 mph.
13
0.553
The mean distance of all home runs hit at 107 mph is fet.
(Round to one decimal place as needed)
14
0.532
15
0.514
16
0.497
(d) IH a ball was hit with a speed of 107 miles per hour, predict how far it will travel
17
0.482
If a bal is hit with a speed of 107 mph, the distance that it is most likely to travel is feet
(Round to one decimal place as needed.)
18
0.468
0.456
19
20
0.444
(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
21
22
0.433
0.423
The ball
farther than the teet that would have been predicted given the speed with which the ball was hit.
23
0.413
(Round to one decimal place as needed.)
24
0404
() Would you feel comfortable using the least-squares regression model on home runs where the speed of the ball was 122 mph? Explain.
25
0.396
26
0388
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.
O B. Yes, because the least squares regression model is the most accurate way to predict the distance of all home runs hit.
OC. 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
OD. 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.
27
0.381
28
0.374
29
0.367
30
0.361
Print
Done
O Time
TCOA2334403
U 108-2014
MAIN
TCGA233DARA
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 basebal game in 2018. Complete parts (a) through (1). Click here to view the data, Click here to view the critical.valu0s of the correlation coefficient .***. (b) Interpret the slope and y-Intercept, if appropriate. Critical values for the correlation coefficient 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 Critical Values for Correlation Coefficient OC. 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. 3. 0.997 4 0.950 Now interpret the y-intercept. 0878 6. 0.811 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, 7 0.754 8 0.707 9 0.666 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 10 0.632 O D. Interpreting the y-Intercept is not appropriate 0.602 12 0.576 (c) Predict the mean distance of all home runs hit at 107 mph. 13 0.553 The mean distance of all home runs hit at 107 mph is fet. (Round to one decimal place as needed) 14 0.532 15 0.514 16 0.497 (d) IH a ball was hit with a speed of 107 miles per hour, predict how far it will travel 17 0.482 If a bal is hit with a speed of 107 mph, the distance that it is most likely to travel is feet (Round to one decimal place as needed.) 18 0.468 0.456 19 20 0.444 (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 21 22 0.433 0.423 The ball farther than the teet that would have been predicted given the speed with which the ball was hit. 23 0.413 (Round to one decimal place as needed.) 24 0404 () Would you feel comfortable using the least-squares regression model on home runs where the speed of the ball was 122 mph? Explain. 25 0.396 26 0388 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. O B. Yes, because the least squares regression model is the most accurate way to predict the distance of all home runs hit. OC. 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 OD. 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. 27 0.381 28 0.374 29 0.367 30 0.361 Print Done O Time TCOA2334403 U 108-2014 MAIN TCGA233DARA
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