What is the relationship between the attendance at a major league ball game and the total number of runs scored? Attendance figures (in thousands) and the runs scored for 8 randomly selected games are shown below. Attendance 50 46 33 50 24 14 27 31 Runs 11 11 8 7 5 2 5 3 Find the correlation coefficient: r=r= Round to 2 decimal places. The null and alternative hypotheses for correlation are: H0:H0: == 0 H1:H1: ≠≠ 0 The p-value is: (Round to four decimal places) Use a level of significance of α=0.05α=0.05 to state the conclusion of the hypothesis test in the context of the study. There is statistically significant evidence to conclude that there is a correlation between the attendance of baseball games and the runs scored. Thus, the regression line is useful. There is statistically insignificant evidence to conclude that there is a correlation between the attendance of baseball games and the runs scored. Thus, the use of the regression line is not appropriate. There is statistically significant evidence to conclude that a game with higher attendance will have fewer runs scored than a game with lower attendance. There is statistically significant evidence to conclude that a game with a higher attendance will have more runs scored than a game with lower attendance. r2r2 = (Round to two decimal places) (Round to two decimal places) Interpret r2r2 : 69% of all games will have the average number of runs scored. There is a 69% chance that the regression line will be a good predictor for the runs scored based on the attendance of the game. There is a large variation in the runs scored in baseball games, but if you only look at games with a fixed attendance, this variation on average is reduced by 69%. Given any fixed attendance, 69% of all of those games will have the predicted number of runs scored. The equation of the linear regression line is: ˆyy^ = + xx (Please show your answers to two decimal places) Use the model to predict the runs scored at a game that has an attendance of 31,000 people. Runs scored = (Please round your answer to the nearest whole number.) Interpret the slope of the regression line in the context of the question: For every additional thousand people who attend a game, there tends to be an average increase of 0.21 runs scored. As x goes up, y goes up. The slope has no practical meaning since the total number runs scored in a game must be positive. Interpret the y-intercept in the context of the question: The y-intercept has no practical meaning for this study. The average runs scored is predicted to be -1. If the attendance of a baseball game is 0, then -1 runs will be scored. The best prediction for a game with 0 attendance is that there will be -1 runs scored.

What is the relationship between the attendance at a major league ball game and the total number of runs scored? Attendance figures (in thousands) and the runs scored for 8 randomly selected games are shown below. Attendance 50 46 33 50 24 14 27 31 Runs 11 11 8 7 5 2 5 3 Find the correlation coefficient: r=r= Round to 2 decimal places. The null and alternative hypotheses for correlation are: H0:H0: == 0 H1:H1: ≠≠ 0 The p-value is: (Round to four decimal places) Use a level of significance of α=0.05α=0.05 to state the conclusion of the hypothesis test in the context of the study. There is statistically significant evidence to conclude that there is a correlation between the attendance of baseball games and the runs scored. Thus, the regression line is useful. There is statistically insignificant evidence to conclude that there is a correlation between the attendance of baseball games and the runs scored. Thus, the use of the regression line is not appropriate. There is statistically significant evidence to conclude that a game with higher attendance will have fewer runs scored than a game with lower attendance. There is statistically significant evidence to conclude that a game with a higher attendance will have more runs scored than a game with lower attendance. r2r2 = (Round to two decimal places) (Round to two decimal places) Interpret r2r2 : 69% of all games will have the average number of runs scored. There is a 69% chance that the regression line will be a good predictor for the runs scored based on the attendance of the game. There is a large variation in the runs scored in baseball games, but if you only look at games with a fixed attendance, this variation on average is reduced by 69%. Given any fixed attendance, 69% of all of those games will have the predicted number of runs scored. The equation of the linear regression line is: ˆyy^ = + xx (Please show your answers to two decimal places) Use the model to predict the runs scored at a game that has an attendance of 31,000 people. Runs scored = (Please round your answer to the nearest whole number.) Interpret the slope of the regression line in the context of the question: For every additional thousand people who attend a game, there tends to be an average increase of 0.21 runs scored. As x goes up, y goes up. The slope has no practical meaning since the total number runs scored in a game must be positive. Interpret the y-intercept in the context of the question: The y-intercept has no practical meaning for this study. The average runs scored is predicted to be -1. If the attendance of a baseball game is 0, then -1 runs will be scored. The best prediction for a game with 0 attendance is that there will be -1 runs scored.

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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Attendance	50	46	33	50	24	14	27	31
Runs	11	11	8	7	5	2	5	3

Find the correlation coefficient: r=r= Round to 2 decimal places.
The null and alternative hypotheses for correlation are:
H0:H0:      == 0
H1:H1:       ≠≠ 0
The p-value is:    (Round to four decimal places)
Use a level of significance of α=0.05α=0.05 to state the conclusion of the hypothesis test in the context of the study.
- There is statistically significant evidence to conclude that there is a correlation between the attendance of baseball games and the runs scored. Thus, the regression line is useful.
- There is statistically insignificant evidence to conclude that there is a correlation between the attendance of baseball games and the runs scored. Thus, the use of the regression line is not appropriate.
- There is statistically significant evidence to conclude that a game with higher attendance will have fewer runs scored than a game with lower attendance.
- There is statistically significant evidence to conclude that a game with a higher attendance will have more runs scored than a game with lower attendance.
r2r2 = (Round to two decimal places) (Round to two decimal places)
Interpret r2r2 :
- 69% of all games will have the average number of runs scored.
- There is a 69% chance that the regression line will be a good predictor for the runs scored based on the attendance of the game.
- There is a large variation in the runs scored in baseball games, but if you only look at games with a fixed attendance, this variation on average is reduced by 69%.
- Given any fixed attendance, 69% of all of those games will have the predicted number of runs scored.
The equation of the linear regression line is:
ˆyy^ = + xx (Please show your answers to two decimal places)
Use the model to predict the runs scored at a game that has an attendance of 31,000 people.
Runs scored = (Please round your answer to the nearest whole number.)
Interpret the slope of the regression line in the context of the question:
- For every additional thousand people who attend a game, there tends to be an average increase of 0.21 runs scored.
- As x goes up, y goes up.
- The slope has no practical meaning since the total number runs scored in a game must be positive.
Interpret the y-intercept in the context of the question:
- The y-intercept has no practical meaning for this study.
- The average runs scored is predicted to be -1.
- If the attendance of a baseball game is 0, then -1 runs will be scored.
- The best prediction for a game with 0 attendance is that there will be -1 runs scored.

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