Regression diagnostics: an introduction

By:

Fox, John

Material type: Text

TextSeries: Quantitative Applications in the Social SciencesPublication details: Sage Publications, Inc. California 2020Edition: 2ndDescription: xv, 151 pISBN:

9781544375229

Subject(s):

DDC classification:

519.536 FOX

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

List(s) this item appears in: Operation & quantitative Techniques | Marketing

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Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Operations Management & Quantitative Techniques	519.536 FOX (Browse shelf(Opens below))	1	Available		003329

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Previous	519.536 ALL Fixed effects regression models	519.536 BOL Regression analysis in R: a comprehensive view for the social sciences	519.536 CHE Statistical regression modeling with R: longitudinal and multi-level modeling	519.536 FOX Regression diagnostics:	519.536 FRO Regression analysis: an intuitive guide for using and interpreting linear models	519.536 HAR Regression modeling strategies:	519.536 LON Regression models for categorical dependent variables using Stata	Next

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

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