Handbook

This course addresses the foundational principles of statistical reasoning. In particular it introduces the most common methods for estimating unknown parameters of key statistical models in order to draw conclusions and make decisions without complete information. Practical implementations using statistical software packages are continuously presented to students. Students explore methods for estimating parameters using Maximum Likelihood and Bayesian inference, for constructing confidence intervals and performing hypothesis testing, for fitting linear and general linear models. Each week, the course includes typically 4 hour of pre-recorded lecture video and 1 hour of face-to-face tutorial during which students will have the opportunity to reinforce their learning.