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Two measures of a baseball player's effectiveness as a hitter are the number of hits he makes in a season and the number of times he “bats in" a run (knows as “Runs Batted In" or RBIS). Can we predict a batter's RBIS from his hits? Below is numerical and graphical output from a computer regression of RBIS on Hits for 12 randomly selected Major League Baseball batters in 2017. Predictor Сoef SE Coef P Constant 14.98 29.35 0.51 0.621 Hits 0.3664 0.1915 1.91 0.085 S = 14.359 R-Sq = 26.8% R-Sq (adj) = 19.5% 110 - 30 - 100 – 20- 90 - 80 - 10 70 - 60 - -10 50 - -20 - 40 120 130 140 150 160 170 180 190 200 -30 -20 -10 10 20 30 120 130 140 150 160 170 180 190 200 Hits Residuals Hits (a) Use the computer output to discuss whether the conditions for regression inference have been met. If you do not have enough information to check a condition, describe what further information would be required. STAN Residual

Two measures of a baseball player's effectiveness as a hitter are the number of hits he makes in a season and the number of times he “bats in" a run (knows as “Runs Batted In" or RBIS). Can we predict a batter's RBIS from his hits? Below is numerical and graphical output from a computer regression of RBIS on Hits for 12 randomly selected Major League Baseball batters in 2017. Predictor Сoef SE Coef P Constant 14.98 29.35 0.51 0.621 Hits 0.3664 0.1915 1.91 0.085 S = 14.359 R-Sq = 26.8% R-Sq (adj) = 19.5% 110 - 30 - 100 – 20- 90 - 80 - 10 70 - 60 - -10 50 - -20 - 40 120 130 140 150 160 170 180 190 200 -30 -20 -10 10 20 30 120 130 140 150 160 170 180 190 200 Hits Residuals Hits (a) Use the computer output to discuss whether the conditions for regression inference have been met. If you do not have enough information to check a condition, describe what further information would be required. STAN Residual

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section4.6: Regression And Median-fit Lines
Problem 17HP
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FRQ 1 Two measures of a baseball player's effectiveness as a hitter are the number of hits he makes in a season and the number of times he “bats in" a run (knows as “Runs Batted In" or RBIS). Can we predict a batter's RBIS from his hits? Below is numerical and graphical output from a computer regression of RBIS on Hits for 12 randomly selected Major League Baseball batters in 2017. Predictor Coef SE Coef T Constant 14.98 29.35 0.51 0.621 Hits 0.3664 0.1915 1.91 0.085 S = 14.359 R-Sq = 26.8% R-Sq (adj) = 19.5% 110 30 100 20 90 - 3 80 10 - 70 - 60 – -10 50 - -20 40 120 130 140 150 160 170 180 190 200 -30 -20 -10 10 20 30 120 130 140 150 160 170 180 190 200 Hits Residuals Hits (a) Use the computer output to discuss whether the conditions for regression inference have been met. If you do not have enough information to check a condition, describe what further information would be required. penpisa Frequency SIAN
Transcribed Image Text:FRQ 1 Two measures of a baseball player's effectiveness as a hitter are the number of hits he makes in a season and the number of times he “bats in" a run (knows as “Runs Batted In" or RBIS). Can we predict a batter's RBIS from his hits? Below is numerical and graphical output from a computer regression of RBIS on Hits for 12 randomly selected Major League Baseball batters in 2017. Predictor Coef SE Coef T Constant 14.98 29.35 0.51 0.621 Hits 0.3664 0.1915 1.91 0.085 S = 14.359 R-Sq = 26.8% R-Sq (adj) = 19.5% 110 30 100 20 90 - 3 80 10 - 70 - 60 – -10 50 - -20 40 120 130 140 150 160 170 180 190 200 -30 -20 -10 10 20 30 120 130 140 150 160 170 180 190 200 Hits Residuals Hits (a) Use the computer output to discuss whether the conditions for regression inference have been met. If you do not have enough information to check a condition, describe what further information would be required. penpisa Frequency SIAN
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