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 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. OA. 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 distance that the ball would travel when the speed that the ball is hit is zero. OC. 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 D. Interpreting the slope is not appropriate. Now interpret the y-intercept.

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.
....
(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.
Data table
y =
(Round to three decimal places as needed.)
(b) Interpret the slope and y-intercept, if appropriate.
Speed (mph)
Distance (feet) D
103.0
393
Begin by interpreting the slope.
103.6
402
98.0
395
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.
100.8
394
O B. 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.
101.4
399
O C. 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.
105.4
418
O D. Interpreting the slope is not appropriate.
101.2
392
101.7
411
Now interpret the y-intercept.
101.3
393
103.3
408
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.
103.5
422
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.
104.8
421
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 103 mph.
Print
Done
The mean distance of all home runs hit at 103 mph is
feet.
(Round to one decimal place as needed.)
(d) If a ball was hit with a speed of 103 miles per hour, predict how far it will travel.
If a ball is hit with a speed of 103 mph, the distance that it is most likely to travel is
(Round to one decimal place as needed.)
feet.
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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. Data table y = (Round to three decimal places as needed.) (b) Interpret the slope and y-intercept, if appropriate. Speed (mph) Distance (feet) D 103.0 393 Begin by interpreting the slope. 103.6 402 98.0 395 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. 100.8 394 O B. 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. 101.4 399 O C. 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. 105.4 418 O D. Interpreting the slope is not appropriate. 101.2 392 101.7 411 Now interpret the y-intercept. 101.3 393 103.3 408 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. 103.5 422 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. 104.8 421 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 103 mph. Print Done The mean distance of all home runs hit at 103 mph is feet. (Round to one decimal place as needed.) (d) If a ball was hit with a speed of 103 miles per hour, predict how far it will travel. If a ball is hit with a speed of 103 mph, the distance that it is most likely to travel is (Round to one decimal place as needed.) feet. Time Remaining: 03:57:09 Next MacBook Pro DII DD B口 888 F9 F10 F11 F7 F4 F5 esc F3 F1 2 E Left
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.
D. interpreting the siope is not appropriate.
Critical values for the correlation
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.
Critical Values for Correlation Coefficie
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.
3
0.997
4
0.950
O D. Interpreting the y-intercept is not appropriate.
0.878
6.
0.811
(c) Predict the mean distance of all home runs hit at 103 mph.
7
0.754
8.
0.707
The mean distance of all home runs hit at 103 mph is
feet.
0.666
(Round to one decimal place as needed.)
10
0.632
(d) If a ball was hit with a speed of 103 miles per hour, predict how far it will travel.
11
0.602
12
0.576
If a ball is hit with a speed of 103 mph, the distance that it is most likely to travel is
(Round to one decimal place as needed.)
feet.
13
0.553
14
0.532
15
0.514
(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.
0.497
16
17
0.482
farther than the
feet that would have been predicted given the speed with which the ball was hit.
The ball
18
0.468
(Round to one decimal place as needed.)
19
0.456
20
0.444
(f) Would you feel comfortable using the least-squares regression model on home runs where the speed of the ball was 122 mph? Explain.
0.433
21
22
0.423
O A. Yes, because the least squares regression model is the most accurate way to predict the distance of all home runs hit.
0.413
23
24
0.404
O B. 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.
25
0.396
C. 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.
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.
Print
Done
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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. D. interpreting the siope is not appropriate. Critical values for the correlation 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. Critical Values for Correlation Coefficie 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. 3 0.997 4 0.950 O D. Interpreting the y-intercept is not appropriate. 0.878 6. 0.811 (c) Predict the mean distance of all home runs hit at 103 mph. 7 0.754 8. 0.707 The mean distance of all home runs hit at 103 mph is feet. 0.666 (Round to one decimal place as needed.) 10 0.632 (d) If a ball was hit with a speed of 103 miles per hour, predict how far it will travel. 11 0.602 12 0.576 If a ball is hit with a speed of 103 mph, the distance that it is most likely to travel is (Round to one decimal place as needed.) feet. 13 0.553 14 0.532 15 0.514 (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. 0.497 16 17 0.482 farther than the feet that would have been predicted given the speed with which the ball was hit. The ball 18 0.468 (Round to one decimal place as needed.) 19 0.456 20 0.444 (f) Would you feel comfortable using the least-squares regression model on home runs where the speed of the ball was 122 mph? Explain. 0.433 21 22 0.423 O A. Yes, because the least squares regression model is the most accurate way to predict the distance of all home runs hit. 0.413 23 24 0.404 O B. 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. 25 0.396 C. 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. 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. Print Done DII DD F12 F10 F11 F9 吕口 F7 F8 F5 F4 F3 F2 LEDN esc F1 %23 2$ 3 4. 5. 6 2 E Left w tab
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