Includes bibliographical references (pages 144-146) and index.
Contents:
Introduction -- The linear regression model : review -- Examining and transforming regression data -- Unusual data : outliers, leverage, and influence -- Nonnormality and nonconstant error variance -- Nonlinearity -- Collinearity -- Diagnostics for generalized linear models -- Concluding remarks.
Summary:
"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 for examples within the text can be found on an accompanying website"-- Provided by publisher.
Series:
Quantitative applications in the social sciences ; volume 79
This resource is supported by the Institute of Museum and Library Services under the provisions of the Library Services and Technology Act as administered by State Library of Iowa.