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r = 0.61, age = 26.0, Sage = 3.44, strikeout = 109.3, S strikeout = 7.48 a) What is the value of b₁, the estimated slope? (Round your answer to 3 decimal places, if needed.) Answer: b) What is the value of bo, the estimated intercept? (Round your answer to 3 decimal places, if needed.) Answer: c) What is the percent of variation in the number of strikeouts that is explained by age, using linear regression? (Round your answer to 2 decimal places, if needed.) Answer: %

r = 0.61, age = 26.0, Sage = 3.44, strikeout = 109.3, S strikeout = 7.48 a) What is the value of b₁, the estimated slope? (Round your answer to 3 decimal places, if needed.) Answer: b) What is the value of bo, the estimated intercept? (Round your answer to 3 decimal places, if needed.) Answer: c) What is the percent of variation in the number of strikeouts that is explained by age, using linear regression? (Round your answer to 2 decimal places, if needed.) Answer: %

MATLAB: An Introduction with Applications
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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Brandon works as a statistician for the Toronto Blue Jays, and wants to analyze the relationship between a player's age and how many strikeouts they accumulate in a season. He takes a sample of 6 Blue Jays players with age between 25 and 34 and finds there is a linear relationship between their ages and the number of strikeouts they had in the 2015 season. Here are the numerical summaries for age and the number of strikeouts: r = 0.61, age = 26.0, Sage = 3.44, strikeout = 109.3, S strikeout = 7.48 a) What is the value of b₁, the estimated slope? (Round your answer to 3 decimal places, if needed.) Answer: b) What is the value of bo, the estimated intercept? (Round your answer to 3 decimal places, if needed.) Answer: c) What is the percent of variation in the number of strikeouts that is explained by age, using linear regression? (Round your answer to 2 decimal places, if needed.) Answer: % d) Can we use this linear regression to predict the number of strikeouts for a player age at 38? O No, because the correlation coefficient is not 1. O Yes, because it is a linear relationship. O No, because we cannot extrapolate. O No, because we are uncertain about the range of the number of strikeouts. O Yes, because we know the slope and intercept values.
Transcribed Image Text:Brandon works as a statistician for the Toronto Blue Jays, and wants to analyze the relationship between a player's age and how many strikeouts they accumulate in a season. He takes a sample of 6 Blue Jays players with age between 25 and 34 and finds there is a linear relationship between their ages and the number of strikeouts they had in the 2015 season. Here are the numerical summaries for age and the number of strikeouts: r = 0.61, age = 26.0, Sage = 3.44, strikeout = 109.3, S strikeout = 7.48 a) What is the value of b₁, the estimated slope? (Round your answer to 3 decimal places, if needed.) Answer: b) What is the value of bo, the estimated intercept? (Round your answer to 3 decimal places, if needed.) Answer: c) What is the percent of variation in the number of strikeouts that is explained by age, using linear regression? (Round your answer to 2 decimal places, if needed.) Answer: % d) Can we use this linear regression to predict the number of strikeouts for a player age at 38? O No, because the correlation coefficient is not 1. O Yes, because it is a linear relationship. O No, because we cannot extrapolate. O No, because we are uncertain about the range of the number of strikeouts. O Yes, because we know the slope and intercept values.
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