FP-asKAcad. year: 2018/2019
Random Variables, Methods of Mathematical Statistics, Pivot tables, Correlation Analysis, Multinomial Regression, Statistical Process Control, Process Capability Indices, Acceptance Sampling, Inventory Management.
Learning outcomes of the course unit
Students will be made familiar with the methods of mathematical statistics, to estimate parameters of regression model, to evaluate a survey, to evaluate and control the production process with methods of statistical process control and process capability index, to evaluate the characteristics of acceptance sampling and inventory management.
Fundamentals of probability theory and mathematical statistics.
Theory of probability, random variables, random vectors.
Recommended optional programme components
Recommended or required reading
ČSN ISO 8258: Shewhartovy regulační diagramy. ČNI Praha, 1993.
KROPÁČ, J. Statistika A. 3. vyd. Brno : FP VUT, 2008. ISBN 978-80-214-3587-2.
TOŠENOVSKÝ, J., NOSKIEVIČOVÁ, D. Statistické metody pro zlepšování jakosti. Ostrava : Montanex a.s. ISBN 80-7225-040-X.
KROPÁČ, J. Statistika B. 2. vyd. Brno : FP VUT, 2009. ISBN 978-80-214-3295-6.
KROPÁČ, J. Statistika. 1. vyd. Brno : FP VUT, 2010. ISBN 978-80-214-3866-8.
KROPÁČ, J. Statistika C. 1. vyd. Brno : FP VUT, 2008. ISBN 978-80-214-3591-9.
Planned learning activities and teaching methods
The course contains lectures that explain basic principles, problems and methodology of the discipline, and exercises that promote the practical knowledge of the subject presented in the lectures.
Assesment methods and criteria linked to learning outcomes
GRADED COURSE-UNIT CREDIT:
The graded course-unit credit will be awarded on the folloving conditions:
- participation in final test
- submitting calculation projects solved on computer.
The mark, which corresponds to the total sum of points achieved (max 100 points), consists of:
- points achieved in control tests,
- points achieved by computer-aided calculation of projects and theoretical questions.
- points achieved in seminars
The grades and corresponding points:
A (100-90), B (89-83), C (82-76), D (75-69), E (68-60), F (59-0).
Language of instruction
Week 1: discrete and continuous random variables (binomial, geometric, hypergeometric, Poisson, alternative, negative binomial distribution, characteristics, probability distributions)
Week 2: continuous random variables (uniform, normal, normalized normal, exponential distribution, density, quantilles)
Week 3: confidence intervals and hypothesis testing (quantiles, construction of point estimates and their interpretation)
Week 4: processing of data files one-dimensional and two-dimensional (measures of location and scattering, mean, median and percentiles, variation range and variance, histogram, empirical distribution function)
Week 5: Pivot tables (general and 2x2 table, test of goodness of fit, pivot tables in Excel)
Week 6: regression analysis (simple and multiple regressiont)
Week 7: regression analysis (the quality of the regression model determination coefficient, t test and F-test)
Week 8: statistical process control measurements (three types of control charts, statistical control of the manufacturing process, identifiable cause)
Week 9: statistical process control comparisons (four basic types of control charts), Target Control Charts
Week 10: capability indices (upper and lower tolerance limit, target value, interpretation capability index)
Week 11: Inventory management (model dependent demand)
Week 12: Inventory management (independent demand model)
Week 13: Acceptance sampling (statistical acceptance inspection measurements and statistical comparisons)
The objective of the course is to learn students to work with the random variables, to work with statistical data sets in Excel, to estimate parameters of regression model in Excel and to interpret these parameters, to evaluate a survez in Excel, to use methods of statistical process control, process capability indices, to evaluate the characteristics of acceptance sampling and inventory management.
Specification of controlled education, way of implementation and compensation for absences
Attendance at lectures is not compulsory but is recommended.
Attendance at exercises is not compulsory but is recommended. and is checked by the tutor.