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Lecturer(s)
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Fiedor David, doc. Mgr. Ph.D.
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Course content
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1. Introduction to work with computer in data analysis, basics of special software for data processing 2. Revision of basic statistical concepts, frequency processing of statistical file 3. Descriptive statistics - calculation of basic statistical characteristics of the whole set and its sub-sets 4. Theoretical distributions and method of estimation of normal frequency distribution parameters 5. Transformation of variables, case selections 6. Introduction to statistical hypothesis testing, methods of data normality verification 7. Parametric and non-parametric tests of hypotheses about distribution parameters 8. Analysis of variance and its nonparametric equivalent 9. Basics of correlation analysis - contingency table and chi square test 10. Revealing the influence of the third variable, partial correlation 11. Basics of regression analysis - focused on linear regression model 12. Repetition
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Learning activities and teaching methods
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Lecture, Demonstration
- Homework for Teaching
- 12 hours per semester
- Preparation for the Exam
- 15 hours per semester
- Attendace
- 24 hours per semester
- Preparation for the Course Credit
- 9 hours per semester
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Learning outcomes
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The Statistical Analysis of data course introduces the students into the fundamental use of mass data using the STATISTICA programme system. The programme is designed for processing quantitative empirical data, creating one's own data sets and transmitting and using other data sets. The basic procedures introduced to the students include work with data sets, one-way classification, creation of contingency tables and use of coefficients for measuring the power of correlation of two variables, hypotheses testing, correlation and regression analysis.
The aim of this course is to acquire basis skills in data analysis using the STATISTICA programme system. It means to create one's own data sets and to use other data sets. Students are able to manage data (construct of indexes etc.), to apply basic statistical procedures (one-way and manifold classification, creation of contingency tables and use of coefficients for measuring the power of association of two variables, correlation and regression analysis) and to interpret the analytical findings.
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Prerequisites
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Attendance of basic statistical course.
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Assessment methods and criteria
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Oral exam, Written exam
Passing two tests (credit) and oral exam
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Recommended literature
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Frankfort-Nachmias, Ch., Leon-Guerrero, A. (2017). Social Statistics for a Diverse Society. Los Angeles: Sage Publications.
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Mareš, P., L. Rabušic, P. Soukup. (2015). Analýza sociálněvědních dat (nejen) v SPSS. Brno: Masarykova univerzita.
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Řezanková, H. (2010). Analýza dat z dotazníkových šetření. Professional Publishing.
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