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Fundamentals of applied statistics

* Exchange students do not have to consider this information when selecting suitable courses for an exchange stay.

Course Unit Code639-2013/02
Number of ECTS Credits Allocated4 ECTS credits
Type of Course Unit *Compulsory
Level of Course Unit *First Cycle
Year of Study *Second Year
Semester when the Course Unit is deliveredWinter Semester
Mode of DeliveryFace-to-face
Language of InstructionCzech
Prerequisites and Co-Requisites Course succeeds to compulsory courses of previous semester
Name of Lecturer(s)Personal IDName
HAL37Ing. Mgr. Petra Halfarová, Ph.D.
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.
Learning Outcomes of the Course Unit
Knowledge of basic statistical methods
Analysis of real data
Ability to process correctly experimental data
Managing work with Excel
Course Contents
1. Introduction to statistics – explanation of its use in metallurgy. Graphical representation of data samples, assessment of data type. General principles of testing.
2. Confirmation of data sample homogeneity using graphs. Outliers – their depiction, detection (box plot) and solution.
3. Confirmation of data independence using graphs. Effect of data dependence on quality of data sample processing.
4. 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.
5. Descriptive characteristics of location, variability, skewness and kurtosis. The notion of robustness of numerical characteristics.
6. 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.
7. Point estimation and confidence intervals. „Confidence level“ and „nivel of test“.
8. Analysis of two data samples. Testing the difference of expected values and variances. Two-sample t-test, F-test.
9. Evaluating a measure of dependence (correlation) of two variables: Pearson’s correlation coefficient, Spearman’s rank correlation coefficient.
10. 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).
11. Regression analysis – multivariate linear regression. Assessment of significance of the model and its regression coefficients. Use of multivariate regression.
Recommended or Required Reading
Required Reading:
JAMES, G., D. WITTEN, T. HASTIE a R. TIBSHIRANI. An Introduction to Statistical Learning. NY: Springer, 2013. ISBN 978-1-4614-7138-7.
SHESKIN, D. J. Handbook of Parametric and Nonparametric Statistical Procedures. NY: Chapman and Hall, 2003. ISBN 1-58488-440-1.
BRUCE, P. and A. BRUCE. Practical Statistics for Data Scientists: 50 Essential Concepts. USA: O´Reilly Media, Inc. 2017. ISBN-13: 978-1491952962
HENDL, Jan. Přehled statistických metod zpracování dat: analýza a metaanalýza dat. Vyd. 2., opr. Praha: Portál, 2006. ISBN 80-7367-123-9.
TOŠENOVSKÝ, J. Základy statistického zpracování dat. Ostrava: VŠB - Technická univerzita Ostrava, 2015. ISBN 978-80-248-3733-8.
MELOUN, Milan a Jiří MILITKÝ. Kompendium statistického zpracování dat. Vyd. 3. Praha: Karolinum, 2012. ISBN 978-80-246-2196-8.
JAMES, G., D. WITTEN, T. HASTIE a R. TIBSHIRANI. An Introductuion to Statistical Learning. NY: Springer, 2013. ISBN 978-1-4614-7138-7.

Recommended Reading:
MONTGOMERY, D. C. Applied Statistics and Probability for Engineers. NY: Wiley, 2010. ISBN-13 978-1-1185-3971-2.

ANDĚL, J. Základy matematické statistiky. Praha: MATFYZPRESS, 2011. ISBN 978-80-737-8162-0.
HANOUSEK, J. a P. CHARAMZA. Moderní metody zpracování dat. Matematická
statistika pro každého. Praha: EDUCA, 1992. ISBN 80-85623-31-5.
TOŠENOVSKÝ, J. a D. NOSKIEVIČOVÁ. Statistické metody pro zlepšování jakosti.
Ostrava: Montanex, 2000. ISBN 80-7225-040-X.
LIKEŠ, J. a J. MACHEK. Matematická statistika. Praha: SNTL, 1983.
Planned learning activities and teaching methods
Lectures, Tutorials
Assesment methods and criteria
Task TitleTask TypeMaximum Number of Points
(Act. for Subtasks)
Minimum Number of Points for Task Passing
Credit and ExaminationCredit and Examination100 (100)51
        CreditCredit40 20
        ExaminationExamination60 31