Peter Martin 
Regression Models for Categorical and Count Data [EPUB ebook] 

Supporto

This text provides practical guidance on conducting regression analysis on categorical and count data. Step by step and supported by lots of helpful graphs, it covers both the theoretical underpinnings of these methods as well as their application, giving you the skills needed to apply them to your own research. It offers guidance on:


·       Using logistic regression models for binary, ordinal, and multinomial outcomes


·       Applying count regression, including Poisson, negative binomial, and zero-inflated models


·       Choosing the most appropriate model to use for your research


·       The general principles of good statistical modelling in practice



Part of The SAGE Quantitative Research Kit, this book will give you the know-how and confidence needed to succeed on your quantitative research journey

€37.99
Modalità di pagamento

Tabella dei contenuti

Introduction

Logistic regression

Ordinal logistic regression: the generalised ordered logit model

Multinomial logistic regression

Regression models for count data

The practice of modelling

Circa l’autore

 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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Lingua Inglese ● Formato EPUB ● Pagine 272 ● ISBN 9781529762686 ● Dimensione 10.0 MB ● Casa editrice SAGE Publications ● Città London ● Paese GB ● Pubblicato 2022 ● Edizione 1 ● Scaricabile 24 mesi ● Moneta EUR ● ID 8325602 ● Protezione dalla copia Adobe DRM
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