https://studiegids.vu.nl/en/courses/2025-2026/B_ADVANMETHThe student understands the basic principles underlying modern statistical techniques, is able to apply them in Python and interpret the results. Main learning objectives:Understand statistics from a model fitting perspectiveUnderstand how to fit contemporary models to dataKnow how to implement model fitting in pythonAn introduction to statistical methods common in modern experimental research. All techniques will be applied to experimental or simulated data in Python. The topics covered in this course are:Basic statistical principles (generative models, estimation, testing, experimental design)Linear regression (simple and multiple regression)Model fitting, model diagnostics and model comparison Analysis of variance (one-way, two-way, interaction)Repeated-measure ANOVALinear Mixed modelsGeneralized linear models (logistic regression and Poisson regression)Lectures and practical computer assignments using jupyter notebooksWeekly reports on data analyses using the PythonLecture slides, notes, jupyter notebooksVUnet or on canvas (for PhD students)Basic mathematics and linear algebra Basic statistics concepts (sampling distributions, hypothesis testing, confidence intervals)Familiarity with common probability distributionsBasic programming skillsBasic computer programming skillsProbability theory (random variable, expectation, variance, probability distributions)Basic linear algebra (matrix notation, matrix multiplication)All information will be available via Canvas