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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.


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

Peter Martin

 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. More About Author