Linear Regression

An Introduction to Statistical Models

AvPeter Martin

Häftad, Engelska, 2022

Del i serien The SAGE Quantitative Research Kit

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2022-03-21
Mått:170 x 242 x 17 mm
Vikt:360 g
Format:Häftad
Språk:Engelska
Serie:The SAGE Quantitative Research Kit
Antal sidor:200
Upplaga:1
Förlag:SAGE Publications
ISBN:9781526424174

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Mer om författaren

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.

Recensioner i media

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.

Innehållsförteckning

What is a statistical modelSimple linear regressionAssumptions and transformationsMultiple linear regression: A model for multivariate relationshipsMultiple linear regression: Inference, assumptions, and standardizationWhere to go from here