Ali S. Hadi

Praise for the Fourth Edition: "This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable." -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded

A bewilderingly large number of statistical quantities have been proposed to study outliers and influence of individual observations in regression analysis. In this article we describe the inter-relationships which exist among the proposed measures. An examination of these relationships leads us to conclude that only three of these measures along with some graphical displays can provide an analyst a complete picture of outliers (major discrepant points) and points which excessively influence the fitted regression equation. Illustrative examples based on real data are presented.

SUMMARY We propose a procedure for the detection of multiple outliers in multivariate data. Let X be an n × p data matrix representing n observations on p variates. We first order the n observations, using an appropriately chosen robust measure of outlyingness, then divide the data set into two initial subsets: A ‘basic’ subset which contains p +1 ‘good’ observations and a ‘non-basic’ subset which contains the remaining n - p - 1 observations. Second, we compute the relative distance from each point in the data set to the centre of the basic subset, relative to the (possibly singular) covariance matrix of the basic subset. Third, we rearrange the n observations in ascending order accordingly, then divide the data set into two subsets: A basic subset which contains the first p + 2 observations and a non-basic subset which contains the remaining n - p - 2 observations. This process is repeated until an appropriately chosen stopping criterion is met. The final non-basic subset of observations is declared an outlying subset. The procedure proposed is illustrated and compared with existing methods by using several data sets. The procedure is simple, computationally inexpensive, suitable for automation, computable with widely available software packages, effective in dealing with masking and swamping problems and, most importantly, successful in identifying multivariate outliers.