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Linear Regression Analysis: Assumptions and Applications

linearRegressionInformation surrounds us; data floods us. Whether presidential poll numbers, statistics on childhood poverty, or the latest box office numbers, we are inundated with figures. Being able to analyze data is important for people who work with data, and statistical analysis is an important tool in this work.

Linear Regression Analysis: Assumptions and Applications, by John P. Hoffmann and Kevin Shafer, 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 and behavioral sciences 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 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.

Chapters include:

  • A Review of Some Elementary Statistical Concepts
  • Simple Linear Regression
  • Multiple Linear Regression Analysis
  • The ANOVA Table and Goodness-of-Fit
  • Comparing Linear Regression Models
  • Dummy Variables in Linear Regression Models
  • Specification Errors in Linear Regression Models
  • Measurement Errors in Linear Regression Models
  • Collinearity and Multicollinearity
  • Nonlinear Associations and Interaction Terms
  • Heteroscedasticity and Autocorrelation
  • Influential Observations: Leverage Points and Outliers
  • An Introduction to Logistical Regression
  • An Introduction to Multilevel Models
  • Conclusion

As the authors point out:

We live in a world where we are surrounded by data. Studies are highlighted in newspapers, magazines, on television, and online daily. We are constantly shown graphs and charts, statistics about life expectancy, crime, pollution, unemployment, life satisfaction, elections, and other phenomena. As a social worker, you will likely encounter data frequently—standardized assessment scores, research studies, and new information as you obtain continuing education units. Understanding statistics, or at least speaking intelligently about them, is practically mandatory for well-educated people and good social workers.




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