Gurantor department | Department of Quality Management | Credits | 10 |

Subject guarantor | prof. RNDr. Josef Tošenovský, CSc. | Subject version guarantor | prof. RNDr. Josef Tošenovský, CSc. |

Study level | postgraduate | Requirement | Choice-compulsory type B |

Year | Semester | winter + summer | |

Study language | English | ||

Year of introduction | 2019/2020 | Year of cancellation | |

Intended for the faculties | FMT | Intended for study types | Doctoral |

Instruction secured by | |||
---|---|---|---|

Login | Name | Tuitor | Teacher giving lectures |

TOS012 | Ing. Filip Tošenovský, Ph.D. | ||

TOS40 | prof. RNDr. Josef Tošenovský, CSc. |

Extent of instruction for forms of study | ||
---|---|---|

Form of study | Way of compl. | Extent |

Full-time | Examination | 20+0 |

Part-time | Examination | 20+0 |

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

Lectures

Individual consultations

Project work

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.

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.

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.

Ústní zkouška s písemnou přípravou.

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

Subject has no prerequisities.

Subject has no co-requisities.

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.

Task name | Type of task | Max. number of points
(act. for subtasks) | Min. number of points |
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Examination | Examination |

Show history

Academic year | Programme | Field of study | Spec. | Zaměření | Form | Study language | Tut. centre | Year | W | S | Type of duty | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

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 sciece and Engineering | K | English | Ostrava | Choice-compulsory type B | study plan | ||||||

2020/2021 | (P0788D270004) Material sciece 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 |

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