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.
