Last updated June 8, 2016
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Linear Regression Analysis

Assumptions and Applications

John P. Hoffmann and Kevin Shafer

978-0-87101-457-3. 2015. Item #4573. 240 pages.

Paperback $46.99   ePub $45.99

Linear Regression Analysis: Assumptions and Applications is designed to provide students with a straightforward introduction to a commonly used statistical model that is appropriate for making sense of data with multiple continuous dependent variables. Using a relatively simple approach that has been proven through several years of classroom use, this text will allow students with little mathematical background to understand and apply the most commonly used quantitative regression model in a wide variety of research settings. Instructors will find that its well-written and engaging style, numerous examples, and chapter exercises will provide essential material that will complement classroom work. Linear Regression Analysis may also be used as a self-teaching guide by researchers who require general guidance or specific advice regarding regression models, by policymakers who are tasked with interpreting and applying research findings that are derived from regression models, and by those who need a quick reference or a handy guide to linear regression analysis.

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.