1. Outliers and Influence Points
NCSS metrics and descriptions

2. Diagnostics for Outliers and High Influence Points
Outliers from the model
Residuals – yi – yihat measures distance from the data to the model
Standardized residual - residual divided by its standard deviation, assuring variance of the observed residuals is constant 2or 3 a priori
Rstudent – standardized residual with sj (root MSE calculated without observation j, also denoted MSEj) rather than s (root MSE) in the denominator.
Outliers in the data (high leverage)
Hat diagonal – see text pages 206 – 208 4/N
High influence points
Cook’s D – attempts to measure the influence of each observation on all N fitted values, i.e. all estimated parameters .5 or 1 or 4/(N-2)

3. Diagnostics for Outliers and High Influence Points
Dffits – attempts to measure the influence of an observation on its individual prediction 1
CovRatio – flags influential observations on the generalized variance of the regression coefficients 1 – 3p/N
DFBETAS - measures the influence of an observation on the estimated BETA coefficient 2/root(N) or 1 or 2