Course detail

Applied Statistic Methodology

FP-SmasPAcad. year: 2020/2021

The course deals with parametric and nonparametric tests, analysis of variance, categorical analysis, multivariate regression models, statistical process control methods and capability indices.

Learning outcomes of the course unit

Students will acquire the knowledge which allows them to use statistical methods at such a theoretical and practical level which allow them to process and perform correct data evaluation and develop the awareness and abilities of students to use statistical methods to manage of company processes.

Prerequisites

Basic knowledge of probability theory, descriptive statistics and mathematical statistics is required.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Studijní materiály vystavené na e-learningu. (CS)
KROPÁČ, J. Statistika A. 4. vyd. Brno: Fakulta podnikatelská, 2011. ISBN 978-80-214-4226-9. (CS)
KROPÁČ, J. Statistika C. 2. vyd. Brno: Akademické nakladatelství CERM, 2012. 100 s. ISBN 978-80-7204-789-5. (CS)
KROPÁČ, J. Statistika B. 2. vyd. Brno: Fakulta podnikatelská, 2009. ISBN 978-80-214-3295-6.
KUPKA, K. Statistické řízení jakosti. Pardubice: TriloByte Statistical Software, 1997. ISBN 80-238-1818-X.
CSN ISO 8258 Shewhartovy regulační diagramy. Praha: Český normalizační institut, 1994. (CS)
MONTGOMERY, D.C. Introduction to Statistical Quality Control. 6 ed. John Wiley & Sons, 2005. ISBN 978-0-470-16992-6.
TOŠENOVSKÝ, J. a D. NOSKIEVIČOVÁ. Statistické metody pro zlepšování jakosti. 1.vyd. Ostrava: Montanex, 2000. ISBN 80-7225-040-X.

Planned learning activities and teaching methods

Teaching consists of lectures that have an explanation of basic principles and methodology of the discipline, pratical problems and their sample solutions.

Exercise promote the practical knowledge of the subject presented in the lectures.

Assesment methods and criteria linked to learning outcomes

The course-unit credit is awarded on the following conditions (max. 40 points):
- submitting answers to calculating problems and theoretical questions.

The exam (max. 60 points)
- has a written form.
In the first part of the exam student solves 4 examples within 80 minutes. In the second part of the exam student works out answers to 3 theoretical questions within 15 minutes.

The mark, which corresponds to the total sum of points achieved (max 100 points), consists of:
- points achieved in control tests, points achieved to calculating questions and theoretical questions,
- points achieved by solving examples,
- points achieved by answering theoretical questions.

The grades and corresponding points:
A (100-90), B (89-80), C (79-70), D (69-60), E (59-50), F (49-0).

Language of instruction

Czech

Work placements

Not applicable.

Course curriculum

Topics of lectures are the following:
1. Parametric statistical tests: t-test, two sample t-test and F-test
2. Kolmogorov-Smirnov test, Pearson test and Shapiro-Wilk test
3. Analysis of variance (ANOVA): one factor and two factor ANOVA
4. Nonparametric statistical tests: one sample tests
5. Nonparametric statistical tests: two sample tests
6. Nonparametric ANOVA
7. Multivariate regression models
8. Multivariate regression models: classical assumptions
9. Categorical analysis
10. Statistical Process Control
11. Control charts for measurement control
12. Control charts for comparison control
13. Process Capability Index

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.

Specification of controlled education, way of implementation and compensation for absences

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.

Classification of course in study plans

  • Programme MGR-SRP Master's, 1. year of study, winter semester, 5 credits, compulsory

Type of course unit

 

Lecture

13 hours, optionally

Teacher / Lecturer

Exercise

26 hours, compulsory

Teacher / Lecturer

eLearning