Binding: Paperback
Description: Data Analysis: A Model Comparison Approach to Regression ANOV a and Beyond is an integrated treatment of data analysis for the social and behavioral sciences. It covers all of the statistical models normally used in such analyses such as multiple regression and analysis of variance but it does so in an integrated manner that relies on the comparison of models of data estimated under the rubric of the general linear model. Data Analysis also describes how the model comparison approach and uniform framework can be applied to models that include product predictors (i. e. interactions and nonlinear effects) and to observations that are nonindependent. Indeed the analysis of nonindependent observations is treated in some detail including models of nonindependent data with continuously varying predictors as well as standard repeated measures analysis of variance. This approach also provides an integrated introduction to multilevel or hierarchical linear models and logistic regression. Finally Data Analysis provides guidance for the treatment of outliers and other problematic aspects of data analysis. It is intended for advanced undergraduate and graduate level courses in data analysis and offers an integrated approach that is very accessible and easy to teach. Highlights of the third edition include: a new chapter on logistic regression; expanded treatment of mixed models for data with multiple random factors; updated examples; an enhanced website with Power Point presentations and other tools that demonstrate the concepts in the book; exercises for each chapter that highlight research findings from the literature; data sets R code and SAS output for all analyses; additional examples and problem sets; and test questions.
Title: Data Analysis A Model Comparison Approach To Regression Anova And Beyond Third Edition
Author(s): Ryan Carey S. (University Of Nebraska At Omaha Usa)
Publisher: Taylor & Francis Ltd
Barcode: 9781138819832
Pages: 378 Pages, 114 Tables, Black And White
Publication Date: 5/8/2017
Category: Probability & Statistics