1. Develop a thorough understanding of multivariate statistical techniques, including their theoretical foundations and practical applications. 2. Learn to apply multivariate statistical techniques to real-world data analysis problems and research questions. 3. Understand the assumptions underlying multivariate statistical methods and how to assess their validity. 4. Gain experience in using statistical software to analyze multivariate data. 5. Develop skills in interpreting and presenting results of multivariate statistical analyses to various audiences.

Hair Jr., J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis. Pearson.

Stevens, J. P. (2009). Applied Multivariate Statistics for the Social Sciences. Routledge.

 Izenman, A. J. (2013). Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning. Springer.

Sharma, S. (1996). Applied Multivariate Techniques. John Wiley & Sons.

Bartholomew, D. J., & Steele, F. (2008). The Analysis of Multivariate Social Science Data. CRC Press

Attendance is crucial for success in this course, and students are expected to attend at least 70% of lectures and seminar sessions.

Class activities: Midterm exam (written), seminar presentation (written and oral) and final exam.

Final course grade is based on 100 points earned through student’s continuous involvement in class activities:

Fair (2) – 50 to 64 points

Good (3) – 65 to 79 points

Very good (4) – 80 to 89 points

Excellent (5) – 90 to 100 points

Earning credits:

Class activities contribute to 50% of the grade:

Seminar – maximum 40 points

Seminar presentation – maximum 10 points

Final exam contributes to 50% of the grade:

Final exam – maximum of 50 points (50% of correct answers necessary for passing)