This group of courses are developed at an undergraduate/master level in statistics. Advance knowledge in both statistics and R is required at the beginning of the courses. Students are expected to be familiar with linear models, GLM and basic inference. It is recommended to take the course Basic concepts in exploratory data analysis (EDA) and computational statistics using R. (See EDA).
- Exploratory multivariate data analysis using R (See Exploratory Data).
The course covers selected topics in multivariate data analysis such as Principal Component Analysis (PCA), Correspondence Analysis (CA),Multiple Correspondence Analysis (MCA) Cluster analysis and Multiple Factor Analysis (MFA). Both slides and YouTube lectures are available.
- Basic concepts in exploratory data analysis (EDA) and computational statistics (see EDA)
This course presents the basic concepts in EDA and computational statistics. Basic Exploratory data analysis in R (Location, Spread, Resistance and Robustness). The course covers selected chapters from the book “Understanding robust and exploratory data analysis” by David C. Hoaglin, Frederick Mosteller and John W. Tukey (Editors).In addtion, the course covers topics related to computational statistics (Basic bootstrap, Likelihood and basic optimization). For the basic bootstrap, the course covers chapters 4-5-6 from the book “An Introduction to the Bootstrap” by B. Efron and R. Tibshirani.
- Resampling based methods using R
The course covers selected chapters from the book of Efron and Tibshitani ‘’an introduction to the bootstrap’’. Topics covered in the course include: basic bootstrap, bootstrap estimation of the standard error, bootstrap confidence intervals, resampling based inference (bootstrap and permutation), bootstrap application for liner and generalized linear models.