Regression guide
Guide to Regression, Nonlinear and Generalized Linear Models
Regression is one of the most popular methods in statistics, and one that is still producing new and exciting techniques. Genstat has a very powerful set of facilities for regression and generalized linear models that are nevertheless very straightforward and easy to use.
This book shows how Genstat’s menus guide you from simple even to very complicated analyses, and also introduces the regression commands that you can use to program any non-standard analyses that you need. We start by explaining ordinary linear regression (with one or several variables), and then extend the ideas to nonlinear models and on to generalized linear models – so that you can analyse counts and proportions as well as the more usual numeric variables. Finally we introduce some of the most recent developments in generalized linear models, including Youngjo Lee and John Nelder’s hierarchical generalized linear models, to bring you fully up-to-date with the range of possibilities.
The chapters cover the following topics.
- Linear regression: ranging from simple linear regression (with one variable) to multiple linear regression (several variables) and the modelling of parallel-line relationships (regression models with groups); plotting of residuals to assess the assumptions, and of the fitted model and data to assess the fit; methods for finding the best models when there are many explanatory variables.
- Nonlinear models: Genstat’s range of standard curves, and the facilities for defining your own nonlinear models.
- Generalized models: how to analyse non-Normal data such as counts and proportions; recent advances – how to use generalized linear mixed models and hierarchical generalized linear models to handle additional sources of random variation.
