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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
· 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
