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Course detail
FP-SmasPAcad. year: 2024/2025
The course deals with parametric and nonparametric tests, analysis of variance, categorical analysis, multivariate regression models, statistical process control methods and capability indices.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Department
Entry knowledge
Rules for evaluation and completion of the course
COURSE COMPLETION
The course-unit credit is awarded on the following conditions (max. 40 points):- preparation of semester assignments (the topic of the assignments will be specified during the semester).The exam (max. 60 points)- has a written form with the possibility of using computer technology and consists of four computational examples and a theoretical question.The grade, which corresponds to the total sum of points achieved (max 100 points), consists of:- points achieved in semester assignments (max. 40 points),- points achieved by solving examples (max. 51 points),- points achieved by answering theoretical questions (max. 9 points).The grade and corresponding points:A (100-90), B (89-80), C (79-70), D (69-60), E (59-50), F (49-0).
Attendance at lectures is not mandatory but is recommended. Attendance at exercises is required and checked by the tutor. An excused absence of a student from seminars can be compensated for by submitting solution of alternate exercises.
Aims
The objective of the course is to learn students with basic principles of mathematical statistics, econometric models, categorical analysis, statistical process control methods and their use in management of company processes.Students will acquire such knowledge that they will be able to master statistical methods at such a theoretical and practical level that will enable them to process data and perform correct evaluation. To develop students' awareness and ability to use statistical tools as a basis for data analysis in the management of individual business activities.
Study aids
see Course literature.Study materials available on e-learning.
Prerequisites and corequisites
Basic literature
Recommended reading
Elearning
Classification of course in study plans
Lecture
Teacher / Lecturer
Syllabus
Topics of lectures are the following:1. Parametric statistical tests: t-test, two sample t-test and F-test2. Kolmogorov-Smirnov test, Pearson test and Shapiro-Wilk test3. Analysis of variance (ANOVA): one factor and two factor ANOVA4. Nonparametric statistical tests: one sample tests5. Nonparametric statistical tests: two sample tests6. Nonparametric ANOVA7. Multivariate regression models8. Multivariate regression models: classical assumptions 9. Categorical analysis10. Statistical Process Control11. Control charts12. Process Capability Indices13. Exploratory analysis of multivariate data
Exercise