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Quantitative Applications in the Social Sciences Ser.: Regression Diagnostics : An Introduction by John Fox (2020, Trade Paperback)
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About this product
Product Identifiers
PublisherSAGE Publications, Incorporated
ISBN-101544375220
ISBN-139781544375229
eBay Product ID (ePID)3038804478
Product Key Features
Number of Pages168 Pages
LanguageEnglish
Publication NameRegression Diagnostics : an Introduction
SubjectMethodology, Probability & Statistics / Regression Analysis, Statistics
Publication Year2020
TypeTextbook
Subject AreaMathematics, Social Science
AuthorJohn Fox
SeriesQuantitative Applications in the Social Sciences Ser.
FormatTrade Paperback
Dimensions
Item Height0.3 in
Item Weight7 Oz
Item Length8.5 in
Item Width5.5 in
Additional Product Features
Edition Number2
Intended AudienceCollege Audience
LCCN2019-028996
ReviewsThe work of a master who knows how to make regression come alive with engaging language and catchy graphics., The work of a master who knows how to make regression come alive with engaging language and catchy graphics. -- Helmut Norpoth * Review * 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. -- William G. Jacoby * Reviewer * 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. -- Peter Marsden * Review *, 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.
Dewey Edition23
IllustratedYes
Dewey Decimal519.536
Table Of ContentSeries Editors IntroductionAbout the AuthorAcknowledgementsChapter 1. IntroductionChapter 2. The Linear Regression Model: Review The Normal Linear Regression Models Least-Squares Estimation Statistical Inference for Regression Coefficients The Linear Regression Model in Matrix FormsChapter 3. Examining and Transforming Regression Data Univariate Displays Transformations for Symmetry Transformations for Linearity Transforming Nonconstant Variation Interpreting Results When Variables are TransformedChapter 4. Unusual data Measuring Leverage: Hatvalues Detecting Outliers: Studentized Residuals Measuring Influence: Cook's Distance and Other Case-Deletion Diagnostics Numerical Cutoffs for Noteworthy Case Diagnostics Jointly Influential Cases: Added-Variable Plots Should Unusual Data Be Discarded? Unusual Data: DetailsChapter 5. Non-Normality and Nonconstant Error Variance Detecting and Correcting Non-Normality Detecting and Dealing With Nonconstant Error Variance Robust Coefficient Standard Errors Bootstrapping Weighted Least Squares Robust Standard Errors and Weighted Least Squares: DetailsChapter 6. Nonlinearity Component-Plus-Residual Plots Marginal Model Plots Testing for Nonlinearity Modeling Nonlinear Relationships with Regression SplinesChapter 7. Collinearity Collinearity and Variance Inflation Visualizing Collinearity Generalized Variance Inflation Dealing With Collinearity *Collinearity: Some DetailsChapter 8. Diagnostics for Generalized Linear Models Generalized Linear Models: Review Detecting Unusual Data in GLMs Nonlinearity Diagnostics for GLMs Diagnosing Collinearity in GLMs Quasi-Likelihood Estimation of GLMs *GLMs: Further BackgroundChapter 9. Concluding Remarks Complementary ReadingReferencesIndex
SynopsisRegression 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.
LC Classification NumberQA278.2.F63 2019
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