# lecture13 - FAQ for Muliple Regression 10:03 FAQ for...

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FAQ for Multiple Regression A baseball analyst would like to develop a model to predict the number of wins during the 2000 baseball season. The analyst collected several variables: Wins, ERA, RunsScored, HitsAllowed, WalksAllowed, Errors, and Saves from 30 professional baseball teams. The are in this linked file. data Summary Output Regression Statistics Multiple R 0.9504 R Square 0.9032 Adj. R Square 0.8877 Standard Error 3.3463 Observations 30 Correlation Matrix Wins ERA Wins 1 ERA -0.6598 1 RunScored 0.6101 0.0856 HitsAllowed -0.5520 0.8600 WalksAllowed -0.2261 0.3105 Saves 0.5320 -0.5985 Errors -0.1308 -0.0540 ANOVA df SS MS F Significance F Regression 4 2611.92 652.98 58.3123 2.59E-12 Residual 25 279.95 11.198 Total 29 2891.87 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept b 0 74.7771 16.7626 4.4610 0.0002 40.2540 109.3003 ERA b 1 -12.3206 3.2066 -3.8423 0.0007 -18.9247 -5.7166 RunScored b 2 0.08433 0.0079 10.6414 9.0114e-11 0.0680 0.1007 HitsAllowed b 3 -0.0107 0.0163 -0.6563 0.5176 -0.0441 0.0228 Saves b 4 0.2731 0.1203 2.2703 0.0321 0.0254 0.5208 Part A: Correlation 1. Which variable is the dependent variable? [go to answer] 2. Which variables are the independent variables? [go to answer] 3.

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