- John Fox - McMaster University, Canada
Regression diagnostics are methods for determining whether a regression model that has been fit to data adequately represents the structure of the data. For example, if the model assumes a linear (straight-line) relationship between the response and an explanatory variable, is the assumption of linearity warranted? Regression diagnostics not only reveal deficiencies in a regression model that has been fit to data but in many instances may suggest how the model can be improved. The Second Edition of this bestselling volume by John Fox considers two important classes of regression models: the normal linear regression model (LM), in which the response variable is quantitative and assumed to have a normal distribution conditional on the values of the explanatory variables; and generalized linear models (GLMs) in which the conditional distribution of the response variable is a member of an exponential family. R code and data sets for examples within the text can be found on an accompanying website at https://tinyurl.com/RegDiag.
The work of a master who knows how to make regression come alive with engaging language and catchy graphics.
This monograph provides very clear and quite comprehensive treatment of many tools and strategies for dealing with the various issues and situations that might arise to compromise the extent to which a regression model accurately represents the structure that exists within a dataset. As such, I would recommend this work to both beginners and experienced researchers in the social sciences.
John Fox has substantially updated his authoritative, compact, and accessible presentation on diagnosing and correcting problems in regression models. New sections on graphical inspection and transformation prior to analysis, and on diagnostics for generalized linear models enhance its utility. I recommend it strongly to instructors and practitioners alike.