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 5 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 = 0.6, age = 25.3, Sage = 3.2, strikeout = 105.7, 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 37? No, because we are uncertain about the range of the number of strikeouts. No, because we cannot extrapolate. Yes, because we know the slope and intercept values. Yes, because it is a linear relationship. No, because the correlation coefficient is not 1.
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 5 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 = 0.6, age = 25.3, Sage = 3.2, strikeout = 105.7, 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 37? No, because we are uncertain about the range of the number of strikeouts. No, because we cannot extrapolate. Yes, because we know the slope and intercept values. Yes, because it is a linear relationship. No, because the correlation coefficient is not 1.
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter10: Statistics
Section10.2: Representing Data
Problem 22PFA
Related questions
Question
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 5 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 = 0.6, age = 25.3, Sage = 3.2, strikeout
=
105.7, 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 37?
No, because we are uncertain about the range of the number of strikeouts.
No, because we cannot extrapolate.
Yes, because we know the slope and intercept values.
Yes, because it is a linear relationship.
No, because the correlation coefficient is not 1.
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