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
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
Related questions
Question
![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 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
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.
0.997
4
0.950
Now interpret the y-intercept.
5
0.878
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.
0.754
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.
0.707
8
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.
11
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
(Round to one decimal place as needed.)
feet.
14
0.532
15
0.514
16
0.497
(d) If a ball was hit with a speed of 107 milles per hour, predict how far it will travel.
17
0.482
If 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.)
18
0.468
feet.
19
0.456
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
0.433
22
0.423
The ball
farther than the
feet 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
0.404
(f) Would you feel comfortable using the least-squares regression model on home runs where the speed of the ball was 122 mph? Explain.
25
0.396
26
0.388
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.
27
0.381
O B. Yes, because the least squares regression model is the most accurate way to predict the distance of all home runs hit
28
0.374
29
0.367
O 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.
30
0.361
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.
Print
Done
O Time
DE
TCOA2334403
u 10-8-2014
MAIN
TCOA2330401](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F993846b8-fce5-49b6-a303-b359ec060b0f%2Fd67eb9f9-42f1-481b-b00c-d28ff4b831a2%2Fyhgs2n_processed.jpeg&w=3840&q=75)
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 corelation 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
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.
0.997
4
0.950
Now interpret the y-intercept.
5
0.878
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.
0.754
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.
0.707
8
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.
11
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
(Round to one decimal place as needed.)
feet.
14
0.532
15
0.514
16
0.497
(d) If a ball was hit with a speed of 107 milles per hour, predict how far it will travel.
17
0.482
If 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.)
18
0.468
feet.
19
0.456
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
0.433
22
0.423
The ball
farther than the
feet 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
0.404
(f) Would you feel comfortable using the least-squares regression model on home runs where the speed of the ball was 122 mph? Explain.
25
0.396
26
0.388
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.
27
0.381
O B. Yes, because the least squares regression model is the most accurate way to predict the distance of all home runs hit
28
0.374
29
0.367
O 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.
30
0.361
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.
Print
Done
O Time
DE
TCOA2334403
u 10-8-2014
MAIN
TCOA2330401
![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 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.
101.7
411
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.
103.6
402
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.
99.3
394
O D. Interpreting the y-intercept is not appropriate.
100.3
392
102.1
392
(c) Predict the mean distance of all home runs hit at 107 mph.
5.4
418
101.2
392
The 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.
Print
Done
If 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 ball
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.)
(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.
DO
TCOA2334403
U 10-8-2014
MAIN
TCOA2330401](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F993846b8-fce5-49b6-a303-b359ec060b0f%2Fd67eb9f9-42f1-481b-b00c-d28ff4b831a2%2Fupwt46v_processed.jpeg&w=3840&q=75)
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 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 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.
101.7
411
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.
103.6
402
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.
99.3
394
O D. Interpreting the y-intercept is not appropriate.
100.3
392
102.1
392
(c) Predict the mean distance of all home runs hit at 107 mph.
5.4
418
101.2
392
The 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.
Print
Done
If 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 ball
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.)
(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.
DO
TCOA2334403
U 10-8-2014
MAIN
TCOA2330401
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