STAT3140 Applied Multivariate Statistics (5 cr)
Cooperation network course
Network: Vaasa Higher Education Consortium
Network: Cross-institutional studies in advanced courses in mathematics and statistics
This course is offered through the Network for Advanced Studies in Mathematics. These studies are available for the following degree students:
- Bachelor's Degree Programme in Mathematics
- Master's Degree Programme in Mathematics
- Bachelor's Degree Programme in Mathematics (Subject Teacher)
- Master's Degree Programme in Mathematics (Subject Teacher)
- Bachelor's Degree Programme in Mathematics, Chemistry or Physics Subject Teacher Education and Primary Teacher Education (Specialication in Mathematics)
- Master's Degree Programme in Mathematics, Chemistry or Physics Subject Teacher Education and Primary Teacher Education (Specialication in Mathematics)
- Doctoral Programme in Mathematics and Statistics
- Doctoral Programme in Mathematics and Science (Specialication in Mathematics)
Description
The course covers (1) supervised statitical learning, including regression, classification, resampling methods, model selection and regulaarization, non-linear models, tree-based methods, support vector machines; (2) unsupervised learning, including principal component analysis and clustering methods.Statistical software: R (https://www.r-project.org/).
Learning outcomes
The participants will learn from practitioners’ point of view principles of statistical machine learning methods, including supervised and unsupervised statistical learning, summarizing (multivariate) statistical data,regression, classification, decision trees, random forests, model selection, re-sampling techniques, model evaluation, and interpreting empirical results of different statistical analyses. The emphasis is in applications of multivariate statistical techniques to data analysis using R software (https://www.r-project.org/), learn to know and utilize the potential of the package and interpret the analysis results of different approaches meaningfully. Course develops critical and analytical thinking, oral and written communication of statistical analysis results and IT skills (R software).
Additional information
Responsible Unit: School of Tehnology and Innovations
Description of prerequisites
Basic statistics (STAT1020 or STAT1030) and Statistical analysis of contingency and regresson (STAT1010), or equivalent.