Being able to choose the most appropriate statistical model for data that typically arise in the digital era.
Getting familiar with the statistical environment R for model estimation and goodness of fit evaluation.
Being able to communicate efficiently the model output.
Getting familiar with the statistical environment R for model estimation and goodness of fit evaluation.
Being able to communicate efficiently the model output.
teacher profile teaching materials
Programme
Introduction to R e R Studio. Importing data. Basic graphs. Basics of descriptive data analysis. Linear models: analysis of variance and regression. Interactions and transformations, Generalized linear models: logistic regression and Poisson regression. Time series analysis: temporal autocorrelation and linear models with ARMA errors. Spatial statistics: spatial autocorrelation and linear models with SAR and CAR errors. Panel data: random effects and generalized linear mixed effects models.Core Documentation
Dobson and Barnett (2008) “An Introduction to Generalized Linear Models” CRC Press (3rd edition).Type of evaluation
Research project and presentation