At the end of the course students should be able:
- to identify the multivariate character of tasks
- be well versed in advanced multivariate statistical methods
- to asses assumptions of methods concerning an application context
- Ekonomicko-správní fakulta
Vyučující
Osnova
Based on students' preferences, topics from the following list will be selected:
Introduction to R environment
An Outline of Different Approaches to Ordinal Data Analysis
Introduction into Multivariate Analysis
Principal Component Analysis
Factor Analysis
Cluster Analysis
Discriminant Analysis
Higher-Order ANOVA
Informace učitele
Data is merely the raw material of knowledge. However, the big issue is the ability of humans to use, analyse and make sense of the data. Most crucial scientific, economic, and business decisions are made based on data analysis which in its complexity can produce valuable information.
When dataset consists of units at which not just one but many (possibly correlated) variables are measured, Multivariate Statistical Analysis provides tools to get meaningful patterns and insights hidden behind the data. Consideration of statistical dependence among variables makes multivariate analysis somewhat different in approach and considerably more complex than the corresponding univariate analysis when there is only one response variable under consideration.
There are three major tasks of multivariate analysis:
• Reduction of multidimensionality (higher number of correlated variables can be represented by a smaller number of representative variables)
• Detection of outliers (with a prospect to multidimensionality which is non-intuitive)
• Looking for patterns and structures hidden in data
This course covers most of the methods used in Multivariate Statistical Analysis (see a list of topics). The aim of this course is not only to offer a theoretical background to these methods but also hands-on experience of real data processing and an interpretation of software outputs within an R environment.
