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
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Chapter1: Combinatorial Analysis
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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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