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a. Find the correlation coefficient: r = b. The null and alternative hypotheses for correlation are: Ho: ? = 0 H₁: ? #0 The p-value is: Round to 2 decimal places. (Round to four decimal places) c. Use a level of significance of a = 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 a higher attendance will have more runs scored than a game with lower attendance. There is statistically significant evidence to conclude that a game with higher attendance will have fewer runs scored than a game with lower attendance. (Round to two decimal places) (Round to two decimal places) d. 7²= e. Interpret 7-²: O 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 56%. There is a 56% chance that the regression line will be a good predictor for the runs scored based on the attendance of the game. 56% of all games will have the average number of runs scored. Given any fixed attendance, 56% of all of those games will have the predicted number of runs scored. f. The equation of the linear regression line is: ŷ= (Please show your answers to two decimal places) g. Use the model to predict the runs scored at a game that has an attendance of 25,000 people. Runs scored = (Please round your answer to the nearest whole number.)

a. Find the correlation coefficient: r = b. The null and alternative hypotheses for correlation are: Ho: ? = 0 H₁: ? #0 The p-value is: Round to 2 decimal places. (Round to four decimal places) c. Use a level of significance of a = 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 a higher attendance will have more runs scored than a game with lower attendance. There is statistically significant evidence to conclude that a game with higher attendance will have fewer runs scored than a game with lower attendance. (Round to two decimal places) (Round to two decimal places) d. 7²= e. Interpret 7-²: O 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 56%. There is a 56% chance that the regression line will be a good predictor for the runs scored based on the attendance of the game. 56% of all games will have the average number of runs scored. Given any fixed attendance, 56% of all of those games will have the predicted number of runs scored. f. The equation of the linear regression line is: ŷ= (Please show your answers to two decimal places) g. Use the model to predict the runs scored at a game that has an attendance of 25,000 people. Runs scored = (Please round your answer to the nearest whole number.)

Calculus For The Life Sciences
Calculus For The Life Sciences
2nd Edition
ISBN:9780321964038
Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Chapter4: Calculating The Derivative
Section4.CR: Chapter 4 Review
Problem 88CR
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f8need help please parts   A,B,D,F,G

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 9 randomly selected games are shown below. Attendance 26 15 21 42 28 28 18 Runs 7 6 4 12 3 9 3 17 12 1 4 a. Find the correlation coefficient: r = b. The null and alternative hypotheses for correlation are: Ho: ?=0 H₁ : ? #0 The p-value is: Round to 2 decimal places. (Round to four decimal places) c. Use a level of significance of a = = 0.05 to state the conclusion of the hypothesis test in the context of the study. O 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. O 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 a higher attendance will have more runs scored than a game with lower attendance. There is statistically significant evidence to conclude that a game with higher attendance will have fewer runs scored than a game with lower attendance. (Round to two decimal places) (Round to two decimal places) d. 7² = e. Interpret 7²: O 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 56%. There is a 56% chance that the regression line will be a good predictor for the runs scored based on the attendance of the game. f. The equation of the linear regression line is: ý = 56% of all games will have the average number of runs scored. Given any fixed attendance, 56% of all of those games will have the predicted number of runs scored. (Please show your answers to two decimal places) g. Use the model to predict the runs scored at a game that has an attendance of 25,000 people. Runs scored = (Please round your answer to the nearest whole number.)
Transcribed Image Text: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 9 randomly selected games are shown below. Attendance 26 15 21 42 28 28 18 Runs 7 6 4 12 3 9 3 17 12 1 4 a. Find the correlation coefficient: r = b. The null and alternative hypotheses for correlation are: Ho: ?=0 H₁ : ? #0 The p-value is: Round to 2 decimal places. (Round to four decimal places) c. Use a level of significance of a = = 0.05 to state the conclusion of the hypothesis test in the context of the study. O 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. O 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 a higher attendance will have more runs scored than a game with lower attendance. There is statistically significant evidence to conclude that a game with higher attendance will have fewer runs scored than a game with lower attendance. (Round to two decimal places) (Round to two decimal places) d. 7² = e. Interpret 7²: O 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 56%. There is a 56% chance that the regression line will be a good predictor for the runs scored based on the attendance of the game. f. The equation of the linear regression line is: ý = 56% of all games will have the average number of runs scored. Given any fixed attendance, 56% of all of those games will have the predicted number of runs scored. (Please show your answers to two decimal places) g. Use the model to predict the runs scored at a game that has an attendance of 25,000 people. Runs scored = (Please round your answer to the nearest whole number.)
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