Course: Applied Statistics 2

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Course title Applied Statistics 2
Course code MUSKM/1AST2
Organizational form of instruction Lecture + Lesson
Level of course Bachelor
Year of study not specified
Semester Winter
Number of ECTS credits 5
Language of instruction Czech
Status of course Compulsory
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Kunčar Aleš, Ing.
  • Homolka Lubor, Ing. Ph.D.
  • Urbánek Tomáš, Ing. Ph.D.
Course content
Course Programme: - Descriptive statistics - fundamental therms (repetition) - Analysis of dependency - introduction - Contingency tables - Analysis of variance - Nonparameric tests - Regression analysis - Correlation analysis - Multiple regression and correlation analysis - Parametric and nonparametric coefficients of correlation - Time series - trend analysis - Time series - moving average - Index theory - Special indexes in CR

Learning activities and teaching methods
Lecturing
learning outcomes
Knowledge
Definuje rozdíl mezi deskriptivní a inferenční statistikou
Definuje rozdíl mezi deskriptivní a inferenční statistikou
Formulate the procedure for evaluating statistical hypotheses
Formulate the procedure for evaluating statistical hypotheses
Choose the right statistical method for evaluating formulated statistical hypotheses
Choose the right statistical method for evaluating formulated statistical hypotheses
Verify the statistical significance of formulated statistical hypotheses
Verify the statistical significance of formulated statistical hypotheses
Skills
Determines the degree of association in a contingency table
Determines the degree of association in a contingency table
Determines the significance of the interaction between categorical and metric variables (t-test, ANOVA)
Determines the significance of the interaction between categorical and metric variables (t-test, ANOVA)
Predict the dependent variable into the future in linear regression modeling
Predict the dependent variable into the future in linear regression modeling
Decides whether or not to reject the null hypothesis based on critical value, p-value and confidence interval.
Decides whether or not to reject the null hypothesis based on critical value, p-value and confidence interval.
Evaluates the result of the given statistical test both statistically and factually.
Evaluates the result of the given statistical test both statistically and factually.
teaching methods
Knowledge
Lecturing
Lecturing
assessment methods
Grade (Using a grade system)
Grade (Using a grade system)
Recommended literature
  • Budíková, M. a kol. Průvodce základními statistickými metodami. Praha: Grada, 2010. ISBN 978-80-247-3243-5.
  • Klímek, Petr. Aplikovaná statistika : cvičení. Vyd. 2. uprav. Zlín : Univerzita Tomáše Bati, 2004. ISBN 807318253X.
  • Klímek, Petr. Aplikovaná statistika pro ekonomy. Vyd. 1. Zlín : Univerzita Tomáše Bati, 2003. ISBN 8073181487.
  • Klímek, Petr. Statistické metody pro ekonomy. Vyd. 1. Zlín : Univerzita Tomáše Bati ve Zlíni, 2001. ISBN 8073180138.
  • Pecáková, I. Statistika v terénních průzkumech. Praha: Professional Publishing, 2008. ISBN 978-80-86946-74-0.
  • Řezanková, H. Analýza dat z dotazníkových šetření. Praha: Professional Pzblishing, 2007. ISBN 978-80-86946-49-8.
  • Seger, Jan. Statistické metody v tržním hospodáoství. 1. vyd. Praha : Victoria Publishing, 1995. ISBN 8071870587.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester