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Lecturer(s)
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Hron Karel, prof. RNDr. Ph.D.
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Course content
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The course is designed for doctoral students of computer science focused on methods of relational data analysis. It provides an overview of selected methods of classical statistical data analysis. - Introduction to multivariate statistics: data display, preprocessing. - Matrix algebra and random vectors. - Selection space geometry and random selection. - Multivariate normal distribution. - Main component method. - Factor analysis. - Factor analysis for binary data. - Cluster analysis.
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Learning activities and teaching methods
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Dialogic Lecture (Discussion, Dialog, Brainstorming), Work with Text (with Book, Textbook)
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Learning outcomes
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The aim of the course is to provide students of doctoral program in Informatics, especially those focused on relational methods of data analysis, education in selected traditional statistical methods of multivariate data analysis. In the course, they will be thoroughly acquainted with the basic concepts of statistical methods and their theoretical foundations.
1. Knowledge Recognize and understand comprehensively principles and methods of multivariate statistical analysis.
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Prerequisites
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unspecified
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Assessment methods and criteria
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Oral exam
Completing the assignments. Passing the exam.
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Recommended literature
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Bartholomew, D.J., Steele, F., Moustaki, I., Galbraith, J. (2008). Analysis of multivariate social science data (2nd edition). Chapman and Hall, London.
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Johnson, R.A., Wichern, D.W. (2007). Applied multivariate statistical analysis (6th edition). Prentica Hall.
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