Linear Regression
An Introduction to Statistical Models
- Peter Martin - University College London, UK, Lecturer in Applied Statistics in the Department of Applied Health Research at University College London.
· Linear regression, including dummy variablesand predictor transformations for curvilinear relationships
· Binary, ordinal and multinomial logistic regression models for categorical data
· Models for count data, including Poisson, negative binomial, and zero-inflated regression
· Checking model assumptions and the dangers of overfitting
Supplements
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. The final chapter, named ‘Where to Go From Here’, suggests some ways in which the reader could deepen their knowledge of regression, and includes the exploration of some paths that could be taken when/if linear regression is not a suitable model. This book is clearly written and accessible to anyone who has previous basic knowledge of descriptive and inferential statistics. Not only does it include flawless text and graphical explanations, but it is also linked with a support website that supplies data sets for most of the examples used. A big plus is the companion examples/exercises for the open-source software R.
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. Running a model can be straightforward, but the author is right to remind us that the results can be misleading if the assumptions of the technique are not assessed. The engaging narrative makes this book welcoming to those without a solid statistical background, but it is still able to provide very relevant insights for the more mathematically inclined.
Sage College Publishing
