Points of Significance: Regression diagnostics - Altman, Naomi; Krzywinski, Martin (Nature methods, 2016)
So far in our discussion of linear regression, we have seen that the estimated regression coefficients and predicted values can be difficult to interpret1. When the predictors are correlated2, the magnitude and even the sign of the estimated regression coefficients can be highly variable, although the predicted values may be stable. When outliers are present3, both the estimated regression coefficients and the predicted values can be influenced. This month, we discuss diagnostics for the robustness of the estimates and of the statistical inferencethat is, the t-tests, confidence intervals and prediction intervals that are computed on the basis of assumptions that the errors are additive, normal and independent and have zero mean and constant variance.