Martin provides a comprehensive account of linear regression and offers a detailed and practical guide on how to interpret all the coefficients and statistics included in a model - a valuable resource for social scientists at all stages in their careers.
The first five chapters set up a clear and solid foundation for understanding statistical models covering a clear explanation of linear regression and its assumptions, the indicators of model fit and predictive power, methods for comparing models with one another as well as complicated cases involving interactions and transformed predictor variables.
This is an excellent introductory text to multivariate analysis of data and is written in accessible language. This text introduces linear regression in a way that is accessible for those with knowledge of descriptive and inferential statistics. The text brings statistical modelling to life while capturing the messiness and ambiguity we may face when interpreting real data. It is engaging and easy to follow. I would highly recommend this for social scientists with an interest in linear regression.
This is a must-have resource for people looking for a clear and complete overview of linear regression. There are many books on the topic but Peter Martin’s Linear regression: an introduction to statistical models is among the few that provided me with a crystal-clear explanation of the technique with real research examples. Additionally, the book deals in detail with an often-overlooked aspect of this type of regression: its assumptions.