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 ( through (1) OA. 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 OB. 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. OC. 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 OD. Interpreting the y-intercept is not appropriate. A (c) Predict the mean distance of all home runs hit at 105 mph. feet. The mean distance of all home runs hit at 105 mph is (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 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 feet that would have been predicted given the speed with which the ball was hit The ball farther than the (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. OA. Yes, because the least squares regression model is the most accurate way to predict the distance of all home runs hit OB. 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 OC. 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 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. Type here to search O Bi li 64°F Cloudy
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 ( through (1) OA. 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 OB. 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. OC. 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 OD. Interpreting the y-intercept is not appropriate. A (c) Predict the mean distance of all home runs hit at 105 mph. feet. The mean distance of all home runs hit at 105 mph is (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 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 feet that would have been predicted given the speed with which the ball was hit The ball farther than the (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. OA. Yes, because the least squares regression model is the most accurate way to predict the distance of all home runs hit OB. 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 OC. 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 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. Type here to search O Bi li 64°F Cloudy
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
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).
OA. 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
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.
OC. 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.
OD. Interpreting the y-intercept is not appropriate.
4
(c) Predict the mean distance of all home runs hit at 105 mph.
feet.
The mean distance of all home runs hit at 105 mph is
(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.
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.)
The ball
(f) Would you feel comfortable using the least-squares regression model on home runs where the speed of the ball was 122 mph? Explain.
OA. Yes, because the least squares regression model is the most accurate way to predict the distance of all home runs hit.
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.
OC. 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.
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.
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Transcribed Image Text:2571335¢erwin=yes
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Critical Values for Correlation Coefficient
n
0.997
0.950
0.878
0.811
0.754
0.707
0.666
0.632
0.602
0.576
0.553
0.532
0.514
0.497
0,482
0.468
0456
0.444
0433
0.423
0413
0,404
0.396
0.388
0.381
0.374
0.367
0.361
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Data Table
64°F Cloudy
Speed (mph)
100.8
102.1
105.4
103.4
101.0
103.5
101.4
103.6
110.4
99.4
100.7
98.0
Print
A
D
Distance (feet)
394
392
418
394
405
395
399
402
427
394
396
395
Done
- X
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