639-3005/04 – Special Statistical Methods (SSM)

Gurantor departmentDepartment of Quality ManagementCredits6
Subject guarantorprof. Ing. Darja Noskievičová, CSc.Subject version guarantorprof. Ing. Darja Noskievičová, CSc.
Study levelundergraduate or graduate
Study languageEnglish
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFMTIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
NOS35 prof. Ing. Darja Noskievičová, CSc.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+4

Subject aims expressed by acquired skills and competences

Students will: - be able to select suitable graphical methods and statistical tests for verification of data presumptions - be able to realize complex statistical data analysis - be able to select suitable method of random sampling - be able to select and apply suitable system of sampling plans - be able to compute a analyze supplier and customer risks - be able to aplly different methods of acceptance sampling for variables - be able to compute false and missing signal and ARL for selected control charts - be able to construct and analyse operational characteristics of selected control charts - be able to select suitable methods of SPC when presumptions for traditional Shewhart control charts are not met - be able to apply nontraditional SPC methods (control charts for nonnormaly distributed data, for autocorrelated data, for multivariable characteristics, for short run processes, for little changes of process parameters).

Teaching methods

Lectures
Tutorials
Project work

Summary

This subject aims to make deeper students´ theoretical basis and practical experience with statistical methods for quality management. A big stress is put on the verification of data pre-conditions, complex solution of problems.Non-traditional methods, especially in the field of statistical process control, are taken into account.

Compulsory literature:

MONTGOMERY, D. C. Statistical quality control: A modern introduction. Hoboken: J. Wiley, 2013. ISBN 978-1118146811. MITRA, A. Fundamentals of quality control and improvement. Hoboken: Wiley, 2016. ISBN: 978-1-118-70514-8. RYAN, T. P. Statistical methods for quality improvement. Hoboken: J. Wiley and Sons, 2011. ISBN 978-1-118-05811-4.

Recommended literature:

NOSKIEVIČOVÁ, D. Special Statistical Methods for Quality Management. Ostrava: VŠB-TUO, 2012. Available from: http://katedry.fmmi.vsb.cz/Opory_FMMI_ENG/QM/Special%20Statistical%20Methods.pdf.

Way of continuous check of knowledge in the course of semester

Combined exam. Elaboration of 2 individual projects. Ongoing verification of preparedness for lessons. Solution of the final test. 80% attendance at lessons.

E-learning

Integrovaný systém modulární počítačové podpory výuky ekonomicko-technického zaměření (http://lms.vsb.cz): Vzdělávací modul 4 – Zlepšování procesů s využitím statistické analýzy:submodul Analýza statistické stability procesu, submodul Postupy analýzy příčin variability, návrhy opatření ke zlepšení, vyhodnocení dosaženého zlepšení. NOSKIEVIČOVÁ, D. Speciální statistické metody – nekonvenční regulační diagramy. Studijní opory. Ostrava: VŠB-TU Ostrava, 2015. NOSKIEVIČOVÁ, D. Speciální statistické metody. Studijní opory. Ostrava: VŠB-TU Ostrava, 2008.BRODECKÁ, K. a kol. Elektronická sbírka příkladů k předmětům zaměřeným na aplikovanou statistiku v rámci studijních plánů oboru Management jakosti. Studijní opory. Ostrava: VŠB-TU Ostrava, 2009. NOSKIEVIČOVÁ, D. Special Statistical Methods for Quality Management. Studijní opory. Doplňkové texty v anglickém jazyce Ostrava: VŠB-TU Ostrava, 2012.

Other requirements

Elaboration of individual projects; running checking tests, final test.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

1. Complex statistical data analysis (verification of normality, homogeneity, data independence – graphical methods). 2. Complex statistical data analysis (verification of normality, homogeneity, data independence - statistical tests). 3. Statistical basis of statistical process control (SPC) 4. Preconditions for the correct application of Shewhart control charts and their verification. 5. Survey and selection of classical Shewhart control charts. 6. Construction and analysis of classical Shewhart control charts. 7. Survey and selection of non-traditional control charts. 8. Control charts for non-normally distributed data. 9. CUSUM and EWMA control charts. 10. Target Short Run control charts. 11. Standardized Short Run control charts. 12. SPC for auto-correlated data. 13. SPC for high-yield processes 14. Basics of acceptance sampling.

Conditions for subject completion

Full-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 40  20
        Examination Examination 60  31
Mandatory attendence parzicipation: 80% presence in teaching

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2020/2021 (N0413A270003) Quality Management and Control of Industrial Systems MPZ P English Ostrava 1 Compulsory study plan
2019/2020 (N0413A270003) Quality Management and Control of Industrial Systems MPZ P English Ostrava 1 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner
FMT+9360 2020/2021 Full-time English Optional 600 - Faculty of Materials Science and Technology - Dean's Office stu. block
FMT-new subjects 2019/2020 Full-time English Optional 600 - Faculty of Materials Science and Technology - Dean's Office stu. block