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Lecturer(s)
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Fačevicová Kamila, Mgr. Ph.D.
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Course content
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1. ANOVA a nonparametric ANOVA 2. Graphical presentation of multivariate dataset, testing of normality and standardization. 3. Principal component analysis. 4. Canonical correlation analysis. Factor analysis. 5. Discrimination analysis. 6. Logistic regression.
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Learning activities and teaching methods
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Dialogic Lecture (Discussion, Dialog, Brainstorming), Demonstration
- Attendace
- 26 hours per semester
- Homework for Teaching
- 30 hours per semester
- Semestral Work
- 30 hours per semester
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Learning outcomes
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Statistical software R and SAS EG - ANOVA, multivariate methods, logistic regression.
Knowledge - Knowledge of procedures and functions for selected statistical methods.
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Prerequisites
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Basic knowledge of software R and SAS EG (data import and manipulations, summarization and data presentation).
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Assessment methods and criteria
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Student performance
Each student has to create a document with examples from seminars and homework.
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Recommended literature
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Online dokumentace softwaru R.
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A. Field, G. Miles. (2010). Discovering Statistics Using SAS. London.
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B. S. Everitt, T. Hothorn. (2006). A Handbook of Statistical Analyses Using R.
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G. Der, B. S. Everitt. (2002). A Handbook of Statistical Analyses Using SAS.
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I. Stankovičová, M. Vojtková. (2007). Viacrozmerné štatistické metódy s aplikáciami. Bratislava.
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J. Verzani. (2005). Using R for Introductory Statistics.
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R. M. Heiberger, B. Holland. (2004). Statistical Analysis and Data Display: An Intermediate Course with Examples in S-Plus, R, and SAS. Springer Texts in Statistics, Springer.
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