Linear Regression Analysis
Subtitle: Assumptions and Applications
Publisher: NASW Press, National Association of Social Workers
Published: May 5, 2015
Place of publication: Washington, USA
Language: eng
Format: Paperback
Number of pages: 240
ISBN-13: 9780871014573
Description: Social work and other social and behavioral science students and researchers need to have a suite of research tools to conduct studies. Regression analysis is a popular tool that is used in numerous studies to examine statistical relationships among variables. Yet there are few books that offer straightforward and easy-to-follow instruction regarding this type of analysis. Most books rely too much on mathematical and symbolic representations of regression analysis, even though many students do not have a sufficient background in mathematics and are often put off by the high level of sophistication required to master these techniques. This book offers a conceptual and software-driven approach to understanding linear regression analysis, with only a slight familiarity with algebra required even for self-study. Students and researchers will find this to be an accessible, yet thorough, introduction to the linear regression model.
Notes: A student who is learning regression for the first time will find how impressing, clear and concise this book is. It goes through stata and spss processes in setting up and interpreting regression data. This book is highly recommended to both students and professors.

Includes bibliographical references and index.
Table of contents: title: -A review of some elementary statistical concepts --; title: -Simple linear regression --; title: -Multiple linear regression analysis --; title: -The ANOVA table and goodness-of-fit --; title: -Comparing linear regression models --; title: -Dummy variables in linear regression models --; title: -Specification errors in linear regression models --; title: -Measurement errors in linear regression models --; title: -Collinearity and multicollinearity --; title: -Nonlinear associations and interaction terms --; title: -Heteroscedasticity and autocorrelation --; title: -Influential observations : leverage points and outliers --; title: -A brief introduction to logistic regression --; title: -An introduction to multilevel models --; title: -Conclusion.