Lecturer(s)


Kubanová Jana, doc. PaedDr. CSc.

Heckenbergerová Jana, Mgr. Ph.D.

Course content

Hypothesis testing  parametric and nonparametric tests, KolmogorovSmirnov test, tests for normal distribution. Analysis of variance, test about population variances. Multidimensional nonparametric tests. Latin squares, GreeceLatin squares, their application. 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. Nonlinear models not transferable to linear form, test of parallelism and identity of the regression lines. 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 . Infringement of basic linear model conditions (heteroscedasticity, tests of heteroscedasticity). Autocorrelation, tests of autocorrelation, multicollinearity. Multidimensional statistic methods  method of basic components, factor analysis, principles, application. Cluster analysis  methods, discriminate analysis.

Learning activities and teaching methods

Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming), Work with text (with textbook, with book)

Learning outcomes

The aims of the subject is to acquaint student with other advanced methods of mathematical statistics, above all multidimensional statistics and with econometrics principlesStudent will learn wide spectra of applications in economic and other socioscientific branches including data processing methodology and data plotting methodology.
Student will able to simulate and evaluate processes associated with economic and social phenomenon.

Prerequisites

Prerequisite for successful mastering of this subject is knowledge of mathematics, probability theory and statistics within the range taught at universities.

Assessment methods and criteria

Oral examination, Written examination, Student performance assessment
Assignmentcompletion of all given tasks and passing the written test. Examinationcomprises of two parts, theoretical and practical. At least 50% success rate is required.

Recommended literature


McClave,J., Benson, P., Sincich, T. Statistics for Bussiness and Economics. New York: Prentice Hall, 2001, 2001.

Mendehall, W.  Sincich, T. Statistics for Engineering and Sciences. New York, Macmillan Publishing Company 1992, 1992. ISBN 002946563X.

Newbold, P. Statistics for Business and Economics. London, PrenticeHall Int. Lim. 1991, 1991. ISBN 0138506450.
