The General Linear Model: A Primer

General Linear Model methods are widely used in data analysis, but there is no compact text that can be used in statistics courses and as a guide. This volume introduces the General Linear Model (GLM) and covers multivariate regression, analysis of variance, analysis under consideration of covariates, variable selection methods, symmetric regression, and recursive partitioning and direction dependence analysis. Real-world data examples illustrate the application of these methods.

Format: Paperback / softback
Length: 125 pages
Publication date: 29 June 2023
Publisher: Cambridge University Press

The General Linear Model (GLM) is a widely employed method in data analysis for applied empirical research. However, a comprehensive and concise text that serves as a reference for statistics courses and data analysis guidance is lacking. This volume aims to address this gap by introducing the GLM, which revolves around the concept that an observed variable can be explained through the combination of weighted independent variables, an additive error term that captures model imperfections and measurement error, and a host of other advanced methods.

The book encompasses a wide range of topics, including multivariate regression, analysis of variance, analysis considering covariates, variable selection techniques, symmetric regression, and the recently developed methods of recursive partitioning and direction dependence analysis. Each method is formally derived and seamlessly integrated into the GLM framework, with specific characteristics emphasized. Real-world data examples are provided to illustrate the practical application of these methods, and guidance is offered on how to interpret the resulting outcomes.

By presenting a comprehensive and accessible introduction to the GLM, this book serves as an invaluable resource for students, researchers, and practitioners in the fields of statistics, economics, and social sciences. It provides a solid foundation for understanding and applying advanced statistical techniques to analyze data and make informed conclusions.

ISBN-13: 9781009322157