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- 0.63, age - 27.7, sage = 3.38, strikeout = 103.4, s strièrout = 7.88 (a) What is the value of b, i.e. the fitted slope? (Round your answer to 3 decimal places) Answer (b) What is the value of by, ie. the fitted intercept? (Round your answer to 3 decimal places.) Answer: (c) What is the percent of variation of the number of strikeouts that is explained by age using a linear regression? (Round your answer to 2 decimal places.) Answer. (d) Can we use this linear regression to predict the number of strikeouts for a player age 40? Answer. O No, because the correlation coefficient is not 1. O Yes, because we know the slope and intercept values. 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.

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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 = 0.63, age = 27.7, sage = 3.38, strikeout = 103.4, satrikegut = 7.88
(a) What is the value of b1, i.e. the fitted slope? (Round your answer to 3 decimal places)
Answer:
(b) What is the value of bg, i.e. the fitted intercept? (Round your answer to 3 decimal places.)
Answer:
(c) What is the percent
variation of the number of strikeouts that is explained by age using a linear regression? (Round your answer to 2 decimal places.)
Answer:
(d) Can we use this linear regression to predict the number of strikeouts for a player age 40?
Answer:
O No, because the correlation coefficient is not 1.
O Yes, because we know the slope and intercept values.
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.
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 = 0.63, age = 27.7, sage = 3.38, strikeout = 103.4, satrikegut = 7.88 (a) What is the value of b1, i.e. the fitted slope? (Round your answer to 3 decimal places) Answer: (b) What is the value of bg, i.e. the fitted intercept? (Round your answer to 3 decimal places.) Answer: (c) What is the percent variation of the number of strikeouts that is explained by age using a linear regression? (Round your answer to 2 decimal places.) Answer: (d) Can we use this linear regression to predict the number of strikeouts for a player age 40? Answer: O No, because the correlation coefficient is not 1. O Yes, because we know the slope and intercept values. 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.
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