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Jul 31st, 2015
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  1. > df.nym = subset(df, tm=="NYM")
  2. > fit <- lm(wins_losses ~ poly(standardized_payroll, 2), data=df.nym)
  3. > summary(fit)
  4.  
  5. Call:
  6. lm(formula = wins_losses ~ poly(standardized_payroll, 2), data = df.nym)
  7.  
  8. Residuals:
  9. Min 1Q Median 3Q Max
  10. -0.18026 -0.02986 0.00273 0.03921 0.14284
  11.  
  12. Coefficients:
  13. Estimate Std. Error t value Pr(>|t|)
  14. (Intercept) 0.5112 0.0117 43.78 <2e-16 ***
  15. poly(standardized_payroll, 2)1 0.0383 0.0639 0.60 0.55
  16. poly(standardized_payroll, 2)2 -0.1761 0.0639 -2.75 0.01 *
  17. ---
  18. Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  19.  
  20. Residual standard error: 0.0639 on 27 degrees of freedom
  21. Multiple R-squared: 0.227, Adjusted R-squared: 0.17
  22. F-statistic: 3.97 on 2 and 27 DF, p-value: 0.0308
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