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Lecturer(s)
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Heckenbergerová Jana, Mgr. Ph.D.
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Course content
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Analysis of variance, test about population variances. Multidimensional nonparametric tests. Regression analysis, multidimensional model of linear regression. Measures of variability for simple linear regression. Confidence intervals for parameters and values of the regression line. Hypothesis testing about values of parameters of the regression line. Infringement of basic linear model conditions (heteroscedasticity, tests of heteroscedasticity). Autocorrelation, tests of autocorrelation, multicollinearity. Non-linear models not transferable to linear form. Logictic regression and its aplications. Correlation analysis. Hypothesis testing about correlation coefficient. Confidence intervals for correlation coefficient. Sample correlation coefficient of partial and multicorrelation. Coeficient of tetrachoric correlation, coeficient biserial correlation. Multidimensional statistic methods - method of basic components, factor analysis, principles, application. Cluster analysis - methods, discriminate analysis.
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming), Work with text (with textbook, with book)
- Preparation for an exam
- 35 hours per semester
- Contact teaching
- 65 hours per semester
- Preparation for a credit (assessment)
- 25 hours per semester
- Home preparation for classes
- 25 hours per semester
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Learning outcomes
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The aims of the subject is to acquaint student with other advanced methods of mathematical statistics, above all multidimensional statistics and with econometrics principles.
Student who has successfully completed the course can: describe real processes using random variables, explain the nature of advanced statistical methods. Student who has successfully completed the course is skilled: to decide correctly about the solution method, to evaluate correctly the conclusions of statistical analyzes, to apply statistical methods, to solve specific tasks, to evaluate data and to interpret conclusions to solve problems in economic and other sciences, including methodology of data processing and evaluation. The student who has successfully completed the course is able to: consider in intention of mathematical statistics, independent solution of data-based problems, using appropriate statistical software to model and evaluate processes related to economic and social phenomena communicate intelligently the results of statistical analysis.
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Prerequisites
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Prerequisite for successful mastering of this subject is knowledge of mathematics, probability theory and statistics within the range taught at universities.
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Assessment methods and criteria
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Oral examination, Written examination, Student performance assessment
Assignment-completion of all given tasks and passing the written test. Examination is written (eventually oral), comprises of two parts, theoretical and practical.
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Recommended literature
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GAYNOR, Patricia E. a KIRKPATRICK, Rickey C. Introduction to time-series modeling and forecasting in business and economics. New York: McGraw-Hill, 1994. ISBN 0-07-034913-4.
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LIND, Douglas A.; MARCHAL, William G. a WATHEN, Samuel A. Statistical techniques in business & economics. New York: McGraw-Hill/Irwin, 2005. ISBN 0-07-111315-0.
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McCLAVE, J., BENSON, P., SINCICH, T. Statistics for Business and Economics. New York Prentice Hall, 2001.
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NEWBOLD, Paul; CARLSON, William L. a THORNE, Betty M. Statistics for business and economics. Harlow, England: Pearson Education, 2023. ISBN 978-1-292-43684-5.
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