639-0940/02 – Statistical Processing of Experimental Data (SZED)

Gurantor departmentDepartment of Quality ManagementCredits10
Subject guarantorIng. Filip Tošenovský, Ph.D.Subject version guarantorIng. Filip Tošenovský, Ph.D.
Study levelpostgraduateRequirementChoice-compulsory type B
YearSemesterwinter + summer
Study languageEnglish
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.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 20+0
Part-time 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

Oral exam with written preparation

E-learning

Other requirements

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

Part-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of pointsMax. počet pokusů
Examination Examination   3
Mandatory attendence participation:

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Conditions for subject completion and attendance at the exercises within ISP:

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Occurrence in study plans

Academic yearProgrammeBranch/spec.Spec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2024/2025 (P0713D070002) Thermal engineering and fuels in industry P English Ostrava Choice-compulsory type B study plan
2024/2025 (P0715D270007) Metallurgical Technology P English Ostrava Choice-compulsory type B study plan
2024/2025 (P0788D270004) Material science and Engineering P English Ostrava Choice-compulsory type B study plan
2023/2024 (P0713D070002) Thermal engineering and fuels in industry P English Ostrava Choice-compulsory type B study plan
2023/2024 (P0713D070002) Thermal engineering and fuels in industry K English Ostrava Choice-compulsory type B study plan
2023/2024 (P0715D270007) Metallurgical Technology K English Ostrava Choice-compulsory type B study plan
2023/2024 (P0715D270007) Metallurgical Technology P English Ostrava Choice-compulsory type B study plan
2023/2024 (P0788D270004) Material science and Engineering P English Ostrava Choice-compulsory type B study plan
2023/2024 (P0788D270004) Material science and Engineering K English Ostrava Choice-compulsory type B study plan
2022/2023 (P0713D070002) Thermal engineering and fuels in industry P English Ostrava Choice-compulsory type B study plan
2022/2023 (P0713D070002) Thermal engineering and fuels in industry K English Ostrava Choice-compulsory type B study plan
2022/2023 (P0715D270007) Metallurgical Technology K English Ostrava Choice-compulsory type B study plan
2022/2023 (P0715D270007) Metallurgical Technology P English Ostrava Choice-compulsory type B study plan
2022/2023 (P0788D270004) Material science and Engineering P English Ostrava Choice-compulsory type B study plan
2022/2023 (P0788D270004) Material science and Engineering K English Ostrava Choice-compulsory type B study plan
2021/2022 (P0713D070002) Thermal engineering and fuels in industry K English Ostrava Choice-compulsory type B study plan
2021/2022 (P0713D070002) Thermal engineering and fuels in industry P English Ostrava Choice-compulsory type B study plan
2021/2022 (P0715D270007) Metallurgical Technology P English Ostrava Choice-compulsory type B study plan
2021/2022 (P0715D270007) Metallurgical Technology K English Ostrava Choice-compulsory type B study plan
2021/2022 (P0788D270004) Material science and Engineering P English Ostrava Choice-compulsory type B study plan
2021/2022 (P0788D270004) Material science and Engineering K English Ostrava Choice-compulsory type B study plan
2020/2021 (P0713D070002) Thermal engineering and fuels in industry K English Ostrava Choice-compulsory type B study plan
2020/2021 (P0713D070002) Thermal engineering and fuels in industry P English Ostrava Choice-compulsory type B study plan
2020/2021 (P0788D270004) Material science and Engineering K English Ostrava Choice-compulsory type B study plan
2020/2021 (P0788D270004) Material science and Engineering P English Ostrava Choice-compulsory type B study plan
2020/2021 (P0715D270007) Metallurgical Technology P English Ostrava Choice-compulsory type B study plan
2020/2021 (P0715D270007) Metallurgical Technology K English Ostrava Choice-compulsory type B study plan
2019/2020 (P0715D270007) Metallurgical Technology K English Ostrava Choice-compulsory type B study plan
2019/2020 (P0715D270007) Metallurgical Technology P English Ostrava Choice-compulsory type B study plan
2019/2020 (P0713D070002) Thermal engineering and fuels in industry K English Ostrava Choice-compulsory type B study plan
2019/2020 (P0713D070002) Thermal engineering and fuels in industry P English Ostrava Choice-compulsory type B study plan

Occurrence in special blocks

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