Linear Regression: An Introduction to Statistical Models / Edition 1

by Peter Martin

Overview

Part of The SAGE Quantitative Research Kit, this text helps you make the crucial steps towards mastering multivariate analysis of social science data, introducing the fundamental linear and non-linear regression models used in quantitative research. Peter Martin covers both the theory and application of statistical models, and illustrates them with illuminating graphs, discussing:

· 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

Product Details

ISBN-13:	9781526424174
Publisher:	SAGE Publications
Publication date:	05/30/2022
Series:	The SAGE Quantitative Research Kit
Pages:	200
Product dimensions:	6.60(w) x 9.50(h) x 0.50(d)

About the Author

Dr Peter Martin is Lecturer in Applied Statistics at University College London. He has taught statistics to students of sociology, psychology, epidemiology, and other disciplines since 2003. One of the joys of being a statistician is that it opens doors to research collaborations with many people in diverse fields. Dr Martin has been involved in investigations in life course research, survey methodology, and the analysis of racism. In recent years his research has focused on health inequalities, psychotherapy, and the evaluation of healthcare services. He has a particular interest in topics around mental health.

Table of Contents

What is a statistical model
Simple linear regression
Assumptions and transformations
Multiple linear regression: A model for multivariate relationships
Multiple linear regression: Inference, assumptions, and standardization
Where to go from here

Editorial Reviews

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.

Jane Elliott

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.

Antonella Cirasola

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.

Dr Sally O'Keeffe

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.

Eliazar Luna