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Lecturer(s)
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Course content
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Exam topics 1) Probability, random variable, probability distributions. 2) Hypothesis testing -- parametric and nonparametric approaches. 3) Regression and correlation analysis - matrix approach, conditions for models, predictions. 4) Goodness of fit tests 5) Introduction to categorical data analysis, Contingency Tables and Chi-Square Test of Independence 6) Logistic regression. 7) Cluster analysis, classification, metrics, hierarchical algorithms. 8) Data visualization. 9) Markov chains - classification, types of models. 10) Markov chains - examples of usage. 11) Markov chains - linear programming methods, optimal strategies. 12) Inventory theory - deterministic models. 13) Inventory theory - stochastic models. 14) Inventory theory - practical usage. 15) Queuing theory - basic principles. 16) Queuing theory - practical usage.
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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 aim is to get a complex view about students knowledge in the courses of different quantitative methods covering statistical methodology for data analysis, time series analysis and models of decision theory.
Students have to prove their complex understanding of the basic terminology and principles of data analysis and decision making, they have to be able to respond to particular questions and to apply theoretical knowledge in examples.
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Prerequisites
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Student know statistical methodology and models for decision making. We suppose that student pass courses focused on basic statistical methodology, time series analysis, advanced course on data analysis (TPS or KTPS,SMAC or KSMAC,STMA or KSTMA) and Models of Decision Theory (RM, KRM, MDT or KMDT).
KMI/STATE and KMI/SCHMO
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Assessment methods and criteria
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Oral examination
Students know well the field of statistic methodology and decision making. They are able to recognize appropriate models from statistic methodology and decision making for their practical economic problems. Propose a solution and evaluate their results.
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Recommended literature
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Dalgaard P. Introductory Statistics with R. Springer, 2002. ISBN 0-387-95475-9.
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Faraway, J. Linear Models with R. Boca Raton : Chapman & Hall/CRC, FL, 2004. ISBN 1-584-88425-8.
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Gros, I. Kvantitativní metody v manažerském rozhodování.. Praha: Grada Publishing, 2003. ISBN 80-247-0421-8.
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Hebák, P. Pravděpodobnostní rozhodování v ekonomických situacích. Praha, VŠE, 1998.
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Hendl J. Přehled statistických metod zpracování dat. Praha: Portál, 2006. ISBN 80-7367-123-9.
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HILLIER F. S., LIEBERMAN G. J. Introduction to Operations Research.. New York: McGraw-Hill, 2010. ISBN 978-007-132483-0.
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Kořenář, V. Stochastické procesy. Praha: VŠE, 2002.
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Maindonald, J., Braun, J. Data Analysis and Graphics Using R. Cambridge : Cambridge University Press, 2003. ISBN 0-521-81336-0.
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Meloun, M., Militký J. Zpracování experimentálních dat. Praha : Plus, 1994.
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Venables, W., N., Ripley, B.D. Modern Applied Statistics with S. New York : 4th ed, 2002. ISBN 0-387-95457-0.
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