Data analysis : a model comparison approach to regression, ANOVA, and beyond /

Bibliographic Details
Main Authors:	Judd, Charles M. (Author), McClelland, Gary H., 1947- (Author), Ryan, Carey S. (Author)
Corporate Author:	ProQuest (Firm)
Format:	eBook
Language:	English
Published:	New York : Routledge, 2017.
Edition:	Third edition.
Subjects:	Mathematical statistics. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Preface
Introduction to data analysis
Simple models: definitions of error and parameter estimates
Simple models: models of error and sampling distributions
Simple models: statistical inferences about parameter values
Simple regression: estimating models with a single continuous predictor
Multiple regression: models with multiple continuous predictors
Moderated and nonlinear regression models
One-way anova: models with a single categorical predictor
Factorial anova: models with multiple categorical predictors and product terms
Ancova: models with continuous and categorical predictors
Repeated measures anova: models with nonindependent errors
Incorporating continuous predictors with nonindependent data: towards mixed models
Outliers and ill-mannered error
Logistic regression
References
Appendices
Author index
Subject index.