Applied Statistics I · Statistics
how to actually do statistics and pitfalls that statisticians fall into
Linear models, Model selection, Mixed effects, Non parametric estimation
a little bit of everything - linear regression, glm, optimization
I feel that I did not gain a structured system of knowledge from this course. Each class felt fragmented, with bits and pieces of information scattered here and there, and the knowledge/time density was too low.
Many different angles on regression, and a thorough view with theoretical backing of what to look for when modeling and what can go wrong.
Linear regression, nonparametric methods, MCMC
Mainly the basics of regression and intuition for analyzing statistical samples.
Some aspects to actual data modelling and that in practice it's proper tough
Linear Regression (including ANOVA): point estimation, interval estimation, hypothesis testing; Model selection (AIC, BIC, etc.); other models like random effects, logistic/LDA, etc. Some data analysis techniques (transformations, etc.)
In this course I learned some heuristics.