Data exploration, regression, GLM & GAM

Data Exploration, Regression, GLM & GAM with introduction to R

 

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We begin with an introduction to R and provide a protocol for data exploration to avoid common statistical problems. We will discuss how to detect outliers, deal with collinearity and transformations. An important statistical tool is multiple linear regression. Various basic linear regression topics will be explained from a biological point of view. We will discuss potential problems and show how generalised linear models (GLM) can be used to analyse count data, presence-absence data and proportional data. Sometimes, parametric models (linear regression, GLM) do not quite fit the data and in such cases generalised additive models (GAM; a smoothing technique) can be used.

During the course, several case studies are presented, in which the statistical theory is integrated with applied analyses in a clear and understandable manner.

Keywords

Introduction to R. Outliers. Transformations. Collinearity (correlation between covariates). Multiple linear regression. Model selection. Visualising results. Poisson GLM. Overdispersion. Negative binomial GLM. Binary and proportional data. ggplot2. Logistic regression.