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Linear Regression
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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.
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March 2022 | 200 pages | SAGE Publications Ltd
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

 
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

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
Professor of Sociology at the University of Exeter

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
Clinical Psychologist

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