541-0134/01 – Statistical Methods for Data Processing (SMTV)

 Gurantor department Department of Geological Engineering Credits 5 Subject guarantor Doc. PaedDr. Vladimír Homola, Ph.D. Subject version guarantor Doc. PaedDr. Vladimír Homola, Ph.D. Study level undergraduate or graduate Study language Czech Year of introduction 2017/2018 Year of cancellation Intended for the faculties HGF Intended for study types Bachelor, Follow-up Master
Instruction secured by
SCH245 Ing. Jarmila Drozdová, Ph.D.
HOM50 Doc. PaedDr. Vladimír Homola, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+2
Combined Credit and Examination 6+6

Subject aims expressed by acquired skills and competences

The aim of the course is to acquaint students with the basics of statistical analysis and essence of the methods of statistical data analysis. The main emphasis is on the issue of the use of traditional and non-parametric methods, their application and practical use. In this course, students will learn rigorous process and evaluate their experimental data. Students will be able to choose appropriate statistical methods, apply them to real data, evaluate and interpret results. An important part of the course is also to familiarize students how to work with statistical software.

Lectures
Tutorials

Summary

The course deals with statistical analysis of experimental data and subsequent interpretations of the results. It provides basic information about the purpose, instruments and methods of statistical analysis. Introduces basic statistical terms, characteristics and relations between them and defines the methods of calculation. An emphasis is placed on the correct choice of methods for evaluating various types of experimental data and interpret results for use in practice. An integral part of the course is also a practical application of methods to real data in software environment for statistical computing (according to the current state of scientific field, for example - Statgraphics, SPSS, SAS, etc.), or in a spreadsheet, during exercises in the computer lab.

Compulsory literature:

MELOUN, Milan a Jiří MILITKÝ. Statistical data analysis: A practical guide with 1250 exercises and answer key on CD. Philadelphia: Woodhead Publishing India Pvt Ltd, 2011. ISBN 978-93-80308-11-1.

Recommended literature:

BERTHOUEX, P. Mac a Linfield C. BROWN. Statistics for environmental engineers. 2nd ed. Boca Raton: Lewis Publishers, 2002. ISBN 15-667-0592-4. OTT, Lyman a Michael LONGNECKER. An introduction to statistical methods & data analysis. 7th ed. Australia: Cengage Learning, 2016. ISBN 978-1-305-26947-7. TRIOLA, Mario F. Essentials of statistics: understanding conventional methods and modern insights. 5th ed. Boston: Pearson, 2015. ISBN 03-219-2459-2.

Way of continuous check of knowledge in the course of semester

Control questions, elaboration and submission of seminary work.

E-learning

Active participation (min. 70%) in seminars and successful completion of credit tests. The exam - oral with written preparation.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

1. Basic statistical terms 2. Set theory – cartesian product, relation, properties of relations 3. Random event and random variable 4. Descriptive and mathematical statistics 5. Exploratory data analysis – graphical techniques and statistical tests 6. Exploratory data analysis – outliers and data transformation 7. Basic statistical characteristics – classical and robust estimators of central tendency and variability 8. Estimators of central tendency and variability for small datasets 9. Interval estimation - confidence interval 10. The significance level 11. Parametric hypothesis tests 12. Nonparametric hypothesis tests 13. Interpolation, extrapolation, approximation 14. Linear regression and correlation analysis

Conditions for subject completion

Full-time form (validity from: 2017/2018 Winter semester)
Min. number of points
Credit and Examination Credit and Examination 100 (100) 51
Credit Credit 33  17
Examination Examination 67  18
Mandatory attendence parzicipation:

Show history
Combined form (validity from: 2017/2018 Winter semester)
Min. number of points
Credit and Examination Credit and Examination 100 (100) 51
Credit Credit 33  17
Examination Examination 67  18
Mandatory attendence parzicipation:

Show history

Occurrence in study plans

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2019/2020 (B2102) Mineral Raw Materials (2102R006) Water Technologies and Water Management P Czech Ostrava 3 Choice-compulsory study plan
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2018/2019 (B2102) Mineral Raw Materials (2102R006) Water Technologies and Water Management P Czech Ostrava 3 Choice-compulsory study plan
2018/2019 (B2102) Mineral Raw Materials (2102R006) Water Technologies and Water Management K Czech Ostrava 3 Choice-compulsory study plan
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2017/2018 (B2102) Mineral Raw Materials (2102R006) Water Technologies and Water Management P Czech Ostrava 3 Choice-compulsory study plan
2017/2018 (B2102) Mineral Raw Materials (2102R006) Water Technologies and Water Management K Czech Ostrava 3 Choice-compulsory study plan
2017/2018 (B2102) Mineral Raw Materials (2102R006) Water Technologies and Water Management K Czech Most 3 Choice-compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner