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        Lecturer(s)
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                    Schindler Martin, Mgr. Ph.D.
                
 
            
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                    Picek Jan, prof. RNDr. CSc.
                
 
            
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                    Slámová Tereza, Mgr. Ph.D.
                
 
            
         
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        Course content
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        Probability - the basic properties and concepts: a random phenomenon, the definition of probability, conditional probability, independence of random events. Random variables: discrete random variable, continuous random variables, distribution functions, characteristics of random variables. Random Vector: distribution function, marginal distribution, independent random variables, conditional distribution, the characteristics of a random vector. Basic concepts of mathematical statistics: random sampling, point and interval estimation, consistency and unbiased estimate, basic hypothesis testing, analysis of variance. Linear regression model: the method of least squares, tests of regression parameters, regression diagnostics. Tests of two vectors of mean values ??(Hotelling tests). Methods of classification Discriminant analysis. Logistic regression. Cluster analysis
         
         
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        Learning activities and teaching methods
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        Monological explanation (lecture, presentation,briefing)
        
            
                    
                
                    
                    - Preparation for exam
                        - 110 hours per semester
                    
 
                
                    
                    - Class attendance
                        - 70 hours per semester
                    
 
                
             
        
        
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                Learning outcomes
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                Knowledge  of advanced methods of mathematical statistics and probability theory  
                 
                Ability to apply advanced methods of mathematical statistics and probability theory
                 
                
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                Prerequisites
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                Mathematics I (MV1) and Mathematics II (MV2) relevant  Bachelor's degree 
                
                
                    
                        
                    
                    
                
                
  
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                Assessment methods and criteria
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                        Oral exam, Written exam
                        
                        
                         
                        
                    
                    
                
                 Active participation in the exercise and obtaining the prescribed points from the final test are necessary for the award of credit.  Requirements on exam: Knowledge of problem solving, concepts and basic ideas.
                 
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        Recommended literature
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                - 
                    Anděl, J. Statistické metody. Matfyzpress: Praha, 2007. ISBN 978-80-7378-003-6.
                
 
            
                
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                    Dalgaard, P. Introductory Statistics with R. 2008. ISBN 978-0-387-79053-4.
                
 
            
                
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                    Hebák, P., Hustopecký, J., Malá, I. Vícerozměrné statistické metody (2). Informatorium, Praha, 2005. ISBN 80-7333-036-9.
                
 
            
                
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                    HENDL, J. Přehled statistických metod. Praha: Portál, 2012. ISBN 978-80-262-0200-4.
                
 
            
                
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                    Kadeřábek J. Statistika. Liberec : Technická univerzita v Liberci, 2006. ISBN 80-7372-044-2.
                
 
            
         
         
         
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