639-0940/01 – Statistical Processing of Experimental Data (SZED)

Gurantor departmentDepartment of Quality ManagementCredits10
Subject guarantorprof. RNDr. Josef Tošenovský, CSc.Subject version guarantorprof. RNDr. Josef Tošenovský, CSc.
Study levelpostgraduateRequirementChoice-compulsory type B
YearSemester
Study languageCzech
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFMTIntended for study typesDoctoral
Instruction secured by
LoginNameTuitorTeacher giving lectures
TOS012 Ing. Filip Tošenovský, Ph.D.
TOS40 prof. RNDr. Josef Tošenovský, CSc.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 20+0
Combined Examination 20+0

Subject aims expressed by acquired skills and competences

Knowledge of elementary methods of mathematical statistics: calculation of basic characteristics, parameter estimation, hypothesis testing, regression and correlation analysis Real data analysis

Teaching methods

Lectures
Individual consultations
Project work

Summary

The subject follows up on probability theory. It uses the tools of probability to present estimation of population parameters, hypothesis testing, modelling of technological processes with regression models and their assessment by correlation analysis. Multivariate regression is taught under the required theoretical conditions. Correlation analysis shows ways of measuring dependence for various types of variables.

Compulsory literature:

JAMES, G., D. WITTEN, T. HASTIE a R. TIBSHIRANI. An Introduction to Statistical Learning. NY: Springer, 2013. ISBN 978-1-4614-7138-7. KUTNER, M. H.,CH. J. NACHTSHEIM and J. NETER. Applied Linear Regression Models. NY:McGraw-Hill, 2004. ISBN 0-07-301344-7. BOX,G. E. P.,HUNTER,W.G.andHUNTER,J.S. Statistics for Experimenters. NY: Wiley&Sons, 1978. ISBN 0-471-09315-7. DRAPER, N. R. and H. SMITH. Applied Regression Analysis. NY: Wiley, 1998. ISBN 978-0471170822. RYAN, T. P. Modern Regression Methods. NY: Wiley, 2008. ISBN 978-0470550441.

Recommended literature:

MONTGOMERY, D. C. Applied Statistics and Probability for Engineers. NY: Wiley, 2010. ISBN-13 978-1-1185-3971-2. SHESKIN, D. J. Handbook of Parametric and Nonparametric Statistical Procedures. NY: Chapman and Hall, 2003. ISBN 1-58488-440-1.

Way of continuous check of knowledge in the course of semester

Ústní zkouška s písemnou přípravou.

E-learning

Další požadavky na studenta

1. Knowledge of basic statistical methods 2. Analysis of real data

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

Introduction to statistics – explanation of its use in metallurgy. Graphical representation of data samples, assessment of data type. General principles of testing. Confirmation of data sample homogeneity using graphs. Outliers – their depiction, detection (box plot) and solution. Confirmation of data independence using graphs. Effect of data dependence on quality of data sample processing. Confirmation of normality: normal distribution, Gauss curve and its parameters, empirical histogram. Reasons why normality is required, and procedures to be followed if the normality condition is not met. Descriptive characteristics of location, variability, skewness and kurtosis. The notion of robustness of numerical characteristics. Student’s distribution, Fisher’s distribution, Pearson’s distribution and their graphs. Examples of using the distributions. Use of tables of quantiles and critical values. Point estimation and confidence intervals. „Confidence level“ and „nivel of test“. Analysis of two data samples. Testing the difference of expected values and variances. Two-sample t-test, F-test. Evaluating a measure of dependence (correlation) of two variables: Pearson’s correlation coefficient, Spearman’s rank correlation coefficient. Regression analysis – simple (paired) linear regression. Estimation of regression coefficients by least squares. Assessment of significance and quality of the regression function. Simple nonlinear regression models (power, exponential, logarithmic, quadratic and polynomial models). Regression analysis – multivariate linear regression. Assessment of significance of the model and its regression coefficients. Use of multivariate regression.

Conditions for subject completion

Combined form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Examination Examination  
Mandatory attendence parzicipation:

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.FormStudy language Tut. centreYearWSType of duty
2019/2020 (P0715D270006) Metallurgical Technology K Czech Ostrava Choice-compulsory type B study plan
2019/2020 (P0715D270006) Metallurgical Technology P Czech Ostrava Choice-compulsory type B study plan
2019/2020 (P0713D070001) Thermal engineering and fuels in industry P Czech Ostrava Choice-compulsory type B study plan
2019/2020 (P0713D070001) Thermal engineering and fuels in industry K Czech Ostrava Choice-compulsory type B study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner