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Lecturer(s)
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Černíková Alena, Mgr. MSc., Ph.D.
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Course content
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1. Multivariate linear regression, diagnostics, transformations and advanced issues in regression modelling. 2. Methods of dimension reduction: principal component analysis, factor analysis. 3. Methods of classification: discriminant analysis, logistic regression. 4. Generalized linear regression models, diagnostics. 5. Bayesian methods: concepts, introduction to Bayesian inference, Bayesian linear regression.
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Learning activities and teaching methods
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unspecified
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Learning outcomes
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The course aims at equipping students with advanced statistical methods applicable in descriptions of natural and socioeconomic phenomena. The course deepens the grasp of linear regression by extending it fully to the multivariate case and reviewing diagnostics and advanced topics that arise in modelling. The course then summarizes basic multivariate statistical methods and gives an introduction to generalized linear models. Finally, the course makes a brief overview of Bayesian statistical analysis and contrasts it with classical (i.e. non-Bayesian) statistics. The ambition is to imprint on students an understanding of statistical thinking. Students are expected to gain practical skills by working with data in program R.
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Prerequisites
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unspecified
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Assessment methods and criteria
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unspecified
Good knowledge of basic statistics is prerequisite for this course.
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Recommended literature
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