Linear Regression: An Introduction to Statistical Models

Author:   Peter Martin
Publisher:   SAGE Publications Ltd
ISBN:  

9781526424174


Pages:   200
Publication Date:   21 March 2022
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Linear Regression: An Introduction to Statistical Models


Overview

This text introduces the fundamental linear regression models used in quantitative research. It covers both the theory and application of these statistical models, and illustrates them with illuminating graphs. The author offers guidence on: Deciding the most appropriate model to use for your research Conducting simple and multiple linear regression Checking model assumptions and the dangers of overfitting Part of The SAGE Quantitative Research Kit, this book will help you make the crucial steps towards mastering multivariate analysis of social science data.

Full Product Details

Author:   Peter Martin
Publisher:   SAGE Publications Ltd
Imprint:   SAGE Publications Ltd
Weight:   0.360kg
ISBN:  

9781526424174


ISBN 10:   1526424177
Pages:   200
Publication Date:   21 March 2022
Audience:   College/higher education ,  Tertiary & Higher Education
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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

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


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


Author Information

 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.

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