(b) Does the estimated regression equation provide a good fit to the data? Explain. Considering the nature of the data, being able to explain slightly --Select--- v than 50% of the variability in the...


(b) Does the estimated regression equation provide a good fit to the data? Explain.<br>Considering the nature of the data, being able to explain slightly --Select--- v than 50% of the variability in the number of runs given up per inning pitched using just two independent variables is<br>--Select---<br>(c) Suppose the earned run average (ERA) is used as the dependent variable in the estimated regression equation from (a) instead of the average number of runs given up per inning pitched. Does the estimated regression equation that uses the ERA provide a good fit<br>to the data? Explain.<br>Since ---Select- v than 50% of the variability in the ERA can be explained by the linear effect of home runs per inning pitched (HR/IP) and strike-outs per inning pitched (SO/IP), this is ---Select---<br>performance.<br>considering the complexity of predicting pitching<br>

Extracted text: (b) Does the estimated regression equation provide a good fit to the data? Explain. Considering the nature of the data, being able to explain slightly --Select--- v than 50% of the variability in the number of runs given up per inning pitched using just two independent variables is --Select--- (c) Suppose the earned run average (ERA) is used as the dependent variable in the estimated regression equation from (a) instead of the average number of runs given up per inning pitched. Does the estimated regression equation that uses the ERA provide a good fit to the data? Explain. Since ---Select- v than 50% of the variability in the ERA can be explained by the linear effect of home runs per inning pitched (HR/IP) and strike-outs per inning pitched (SO/IP), this is ---Select--- performance. considering the complexity of predicting pitching
Major League Baseball (MLB) pitching statistics were reported for a random sample of 20 pitchers from the American League for one full season.<br>Player<br>Team<br>w<br>L<br>ERA<br>so/IP<br>HR/IP<br>R/IP<br>Verlander, J<br>DET<br>24<br>2.40<br>1.00<br>0.10<br>0.29<br>Beckett, J<br>BOS<br>13<br>7<br>2.89<br>0.91<br>0.11<br>0.34<br>Wilson, C<br>TEX<br>16<br>7<br>2.94<br>0.92<br>0.07<br>0.40<br>Sabathia, C<br>NYY<br>19<br>8<br>3.00<br>0.97<br>0.07<br>0.37<br>Haren, D<br>LAA<br>16<br>10<br>3.17<br>0.81<br>0.08<br>0.38<br>McCarthy, B<br>OAK<br>9<br>9<br>3.32<br>0.72<br>0.06<br>0.43<br>Santana, E<br>LAA<br>11<br>12<br>3.38<br>0.78<br>0.11<br>0.42<br>Lester, J<br>BOS<br>15<br>9<br>3.47<br>0.95<br>0.10<br>0.40<br>Hernandez, F<br>SEA<br>14<br>14<br>3.47<br>0.95<br>0.08<br>0.42<br>Buehrle, M<br>CWS<br>13<br>9<br>3.59<br>0.53<br>0.10<br>0.45<br>Pineda, M<br>SEA<br>9<br>10<br>3.74<br>1.01<br>0.11<br>0.44<br>Colon, B<br>NYY<br>8<br>10<br>4.00<br>0.82<br>0.13<br>0.52<br>Tomlin, J<br>CLE<br>12<br>7<br>4.25<br>0.54<br>0.15<br>0.48<br>Pavano, C<br>MIN<br>9<br>13<br>4.30<br>0.46<br>0.10<br>0.55<br>Danks, J<br>CWS<br>8<br>12<br>4.33<br>0.79<br>0.11<br>0.52<br>Guthrie, J<br>BAL<br>9<br>17<br>4.33<br>0.63<br>0.13<br>0.54<br>Lewis, C<br>TEX<br>14| 10<br>4.40<br>0.84<br>0.17<br>0.51<br>Scherzer, M<br>DET<br>15<br>9<br>4.43<br>0.89<br>0.15<br>0.52<br>Davis, W<br>TB<br>11<br>10<br>4.45<br>0.57<br>0.13<br>0.52<br>Porcello, R<br>DET<br>14<br>9<br>4.75<br>0.57<br>0.10<br>0.57<br>(a) An estimated regression equation was developed relating the average number of runs given up per inning pitched given the average number of strikeouts per inning pitched and the average number of home runs per inning pitched. What are the values of<br>R and R_? (Round your answers to four decimal places.)<br>R2 =<br>R.<br>=<br>

Extracted text: Major League Baseball (MLB) pitching statistics were reported for a random sample of 20 pitchers from the American League for one full season. Player Team w L ERA so/IP HR/IP R/IP Verlander, J DET 24 2.40 1.00 0.10 0.29 Beckett, J BOS 13 7 2.89 0.91 0.11 0.34 Wilson, C TEX 16 7 2.94 0.92 0.07 0.40 Sabathia, C NYY 19 8 3.00 0.97 0.07 0.37 Haren, D LAA 16 10 3.17 0.81 0.08 0.38 McCarthy, B OAK 9 9 3.32 0.72 0.06 0.43 Santana, E LAA 11 12 3.38 0.78 0.11 0.42 Lester, J BOS 15 9 3.47 0.95 0.10 0.40 Hernandez, F SEA 14 14 3.47 0.95 0.08 0.42 Buehrle, M CWS 13 9 3.59 0.53 0.10 0.45 Pineda, M SEA 9 10 3.74 1.01 0.11 0.44 Colon, B NYY 8 10 4.00 0.82 0.13 0.52 Tomlin, J CLE 12 7 4.25 0.54 0.15 0.48 Pavano, C MIN 9 13 4.30 0.46 0.10 0.55 Danks, J CWS 8 12 4.33 0.79 0.11 0.52 Guthrie, J BAL 9 17 4.33 0.63 0.13 0.54 Lewis, C TEX 14| 10 4.40 0.84 0.17 0.51 Scherzer, M DET 15 9 4.43 0.89 0.15 0.52 Davis, W TB 11 10 4.45 0.57 0.13 0.52 Porcello, R DET 14 9 4.75 0.57 0.10 0.57 (a) An estimated regression equation was developed relating the average number of runs given up per inning pitched given the average number of strikeouts per inning pitched and the average number of home runs per inning pitched. What are the values of R and R_? (Round your answers to four decimal places.) R2 = R. =
Jun 11, 2022
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