541-0135/01 – Advanced Statistical Methods for Data Processing (PSM)

Gurantor departmentDepartment of Geological EngineeringCredits5
Subject guarantorDoc. PaedDr. Vladimír Homola, Ph.D.Subject version guarantorDoc. PaedDr. Vladimír Homola, Ph.D.
Study levelundergraduate or graduateRequirementChoice-compulsory
Year2Semesterwinter
Study languageCzech
Year of introduction2017/2018Year of cancellation2018/2019
Intended for the facultiesHGFIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
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
Part-time Credit and Examination 6+6

Subject aims expressed by acquired skills and competences

The aim of the course is to acquaint student with advanced methods of statistical data analysis that are currently widely used, not only in science and research, but also in the application sphere. These methods include mainly correlation and regression analysis, statistical hypothesis testing, analysis of variance (ANOVA), multivariate statistical analysis, modelling and visualization of monitored variables in 2D and 3D view etc. The main emphasis is on understanding the nature of advanced statistical methods, their application and practical use. In this course, students will learn to rigorously carry out an exploratory and confirmatory data analysis, including verification of the preconditions for the subsequent application of statistical methods mentioned above. An important part of the course is the student's own work with experimental data in an appropriate statistical software, subsequent evaluation and interpretation of the results.

Teaching methods

Lectures
Tutorials

Summary

The course deals with the application of advanced methods of statistical data analysis and subsequent interpretations of obtained results. It explains the mathematical principles of various statistical methods and defines the basic conditions for their use. An emphasis is placed on the correct choice of methods in order to obtain the greatest amount of relevant information and knowledge for subsequent 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, Surfer etc.) 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. RENCHER, Alvin C. a William F. CHRISTENSEN. Methods of multivariate analysis. 3rd ed. Hoboken: Wiley, 2012. Wiley series in probability and statistics. ISBN 978-0-470-17896-6.

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. DEVORE, Jay L. a Kenneth N. BERK. Modern mathematical statistics with applications. 2nd ed. London: Springer, 2012. Springer texts in statistics. ISBN 14-614-0390-1. MCKILLUP, Steve a M. Darby DYAR. Geostatistics explained: an introductory guide for earth scientists. Cambridge: Cambridge University Press, 2010. ISBN 978-0-521-74656-4.

Way of continuous check of knowledge in the course of semester

Control questions, elaboration and submission of seminary work.

E-learning

Other requirements

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. Introduction to statistical data analysis 2. The issue writing of values (decimal places), rounding and missing values (limit of detection) 3. Exploratory data analysis – graphical techniques, outliers, statistical tests and data transformation 4. Confirmatory data analysis – classical and robust estimators of central tendency and variability, small datasets 5. Statistical hypothesis testing – parametric and nonparametric hypothesis tests 6. Linear regression and correlation analysis 7. Analysis of variance (ANOVA) – one-way ANOVA 8. Analysis of variance (ANOVA) – two-way ANOVA and MANOVA 9. Multivariate statistical methods – basic assumptions 10. Cluster analysis 11. Principal component analysis 12. Factor analysis 13. Two-dimensional interpolation, extrapolation and approximation 14. Three-dimensional Interpolation, extrapolation, approximation

Conditions for subject completion

Part-time form (validity from: 2017/2018 Winter semester, validity until: 2018/2019 Summer semester)
Task nameType of taskMax. number of points
(act. for subtasks)
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

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2017/2018 (N2102) Mineral Raw Materials (3904T029) Mineral Biotechnology P Czech Ostrava 2 Choice-compulsory study plan
2017/2018 (N2102) Mineral Raw Materials (2102T006) Water Technologies and Water Management K Czech Ostrava 2 Choice-compulsory study plan
2017/2018 (N2102) Mineral Raw Materials (2102T006) Water Technologies and Water Management K Czech Most 2 Choice-compulsory study plan
2017/2018 (N2102) Mineral Raw Materials (2102T006) Water Technologies and Water Management P Czech Ostrava 2 Choice-compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner