Multivariate Data Analysis

Learning Outcomes

  1. Define main notions in the multivariate data analysis
  2. Explain mathematical backgrounds of main multivariate statistical procedures
  3. Apply linear multiple regression analysis
  4. Differentiate between principal component analysis and factor analysis
  5. Justify the adequacy of different multivariate statistical methods for various problems
  6. Interpret the results of multivariate statistical data analysis and explain their practical meaning

Forms of Teaching

Lectures

Independent assignments

Laboratory

Week by Week Schedule

  1. Objective of multivariate statistical analysis; Data, objects, variables and scales (Stevens's classification); Classification of multivariate techniques, Summarizing, describing and graphical representation of multivariate data
  2. Data manipulation prior to multivariate analysis (missing data, outlier detection, transformations of data, standardization, normality, linearity, homoscedascity, homoegenity), Data appropriate for multivariate analysis: data, correlation, variance-covariance, sum-of-squares and cross-products matices, residuals; distances (statistical and Mahalanobis)
  3. Sample geometry and Random sampling
  4. Applied correlation and regression analysis, interpretation and relation to ANOVA, Canonical correlation analysis
  5. Discriminant analysis
  6. Logistic regression
  7. Principal component analysis
  8. Midterm exam
  9. Exploratory factor analysis
  10. Cluster analysis
  11. Multidimensional scaling
  12. Correspondence analysis
  13. Survival analysis/Failure analysis
  14. The Lasso method for high dimensional data (Lasso for linear models, generalized linear models and the Lasso, group Lasso)
  15. Final exam

Study Programmes

University graduate
Audio Technologies and Electroacoustics (profile)
Free Elective Courses (2. semester)
Communication and Space Technologies (profile)
Free Elective Courses (2. semester)
Computational Modelling in Engineering (profile)
Free Elective Courses (2. semester)
Computer Engineering (profile)
Free Elective Courses (2. semester)
Computer Science (profile)
Free Elective Courses (2. semester)
Control Systems and Robotics (profile)
Free Elective Courses (2. semester)
Data Science (profile)
(2. semester)
Electrical Power Engineering (profile)
Free Elective Courses (2. semester)
Electric Machines, Drives and Automation (profile)
Free Elective Courses (2. semester)
Electronic and Computer Engineering (profile)
Free Elective Courses (2. semester)
Electronics (profile)
Free Elective Courses (2. semester)
Information and Communication Engineering (profile)
Free Elective Courses (2. semester)
Network Science (profile)
Free Elective Courses (2. semester)
Software Engineering and Information Systems (profile)
Free Elective Courses (2. semester)

Literature

(.), 1. Johnson, R. A., and D. W. Wichern, Applied Multivariate Statistical Analysis, 5th Edition, Prentice Hall (2002),
(.), 2. B.G. Tabachnick, L.S. Fidell, Using multivariate statistics, 6th Edition, Pearson (2018),
(.), 3. Hair J.F. et al. Multivariate Data Analysis, 7th Edition, Pearson (2014).,

For students

General

ID 222481
  Summer semester
5 ECTS
L3 English Level
L1 e-Learning
45 Lectures
15 Exercises
6 Laboratory exercises

Grading System

Excellent
Very Good
Good
Acceptable