In this presentation we will give an overview on Linear Statistical Models. We start by introducing some multivariate analysis methods such as Principal Components, Factor Analysis and Cluster Analysis. Then, we proceed to discuss several extensions of the Classical Linear Models, starting by describing Generalized Linear Models (GLMs). GLMs allow the response probability distribution to be any member of an exponential family of distributions and still rely on the assumption that the effect of covariates can be modelled through a linear predictor. The last part of this presentation will be dedicated to the Generalized Linear Mixed Models (GLMMs), which are an extension of GLMs by adding random effects in the linear predictor term to a regression setting.