About this manual
Many applications on statistical data are aimed to produce models that “describe” and try to “predict” the relationship between a set of explicative variables and one (or more) dependent variable.
Also when the goal of an analysis is not focused on describing or predict phenomena (as, for example, in ANOVA applications or in t-test), often “behind the scenes” a statistical model is the “core” of the analysis.
The analytical philosophy in R is then strongly model-oriented, also when it seems not.
The main aim of this manual is then on discovering modeling capabilities of R language via practical applications, and on approaching the main functions used to develop Linear Models (LM) and Generalized Linear Models (GLM).
The manual is split in four sections: Linear Models, Generalized Linear Models and Other Generalized Linear Models, Other models and Appendices.
Almost all sections initially show a theoretical introduction on the topic covered, and then several applied examples where many problems are addressed by means of models.
- T-test, Anova
- Inferential Statistics
- Complex Linear Models
- Some Theory on LM
Generalized Linear Models
- Some Theory on GLM
- Multinomial Logistic
- Ordered Logit Model
- Negative Binomial Model
- Some Theory on other GLM
When and where
“Raccoon – Statistical Models with R” will be published one chapter a week in our Quantide blog.