Preface to First Edition - lib

SUMMARY 113R> plot(clouds_fitted, clouds_resid, xlab = "Fitted values",+ ylab = "Residuals", type = "n",+ ylim = max(abs(clouds_resid)) * c(-1, 1))R> abline(h = 0, lty = 2)R> text(clouds_fitted, clouds_resid, labels = rownames(clouds))15Residuals−4 −2 0 2 422 231119247852118174201332141216691010 2 4 6 8 10Fitted valuesFigure 6.7Plot of residuals against fitted values for clouds seeding data.part of any regression analysis involves the graphical examination of residualsand other diagnostic statistics to help identify departures from assumptions.ExercisesEx. 6.1 The simple residuals calculated as the difference between an observedand predicted value have a distribution that is scale dependent since thevariance of each is a function of both σ 2 and the diagonal elements of the© 2010 by Taylor and Francis Group, LLC