Data Processing - Prognoses and Risk Assessment

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Code Completion Credits Range
16RISK Z,ZK 5 3P+2C
Radek Černý
Radek Černý
Department of Dosimetry and Application of Ionizing Radiation

The aim of the course is to acquaint students with the theoretical basis necessary for description and processing of experimental data. Theoretical knowledge is then applied to illustrative examples of practical data processing, and students will learn how to use available software for experimental data processing. In addition, the aim of the course is to acquaint students with tools for risk analysis and their qualitative and quantitative evaluation.

Syllabus of lectures:

1.Introduction to data processing - representative data, basic data description, graphical presentation of experimental data.

2.Uncertainties of measurement and data processing, classification of phenomena contributing to the uncertainty of results, quantification of uncertainties.

3.Basic statistical data processing and exploratory analysis of one-dimensional data - description and properties of a one-dimensional data set, verification of assumptions about the data set, statistical analysis of data.

4.Selection methods, basic statistical distributions, the transformation of date, point and interval estimation, confidence intervals.

5.Linear regression models, correlation, nonlinear regression models.

6.Statistical hypothesis testing, analysis of variance, correlation analysis.

7.Descriptive statistics of multidimensional data and exploratory analysis, basic statistical analysis of multidimensional data.

8.Classical interpolation techniques, spline interpolation, approximation by function and polynomial, data smoothing.

9.Qualitative risk analysis, identification and description of risks, identification of sources of risk.

10.Quantitative risk analysis, assessment and estimates of the probability of threats and consequences.

11.Risk assessment, planning of activities to prevent and eliminate risks.

Syllabus of tutorials:

1.SW for statistical data processing and visualization.

2.Graphical processing and presentation of data, demonstration of available SW with a focus on open-source SW.

3.Basic statistical processing and analysis of data in R-software.

4.Basic statistical processing and analysis of data in R-software.

5.Graphical visualization of data in R-software

6.Testing of data normality, transformation of data, calculation of basic statistics.

7.Graphical visualization of data in dependence on the geographical position - application QGIS software.

8.Calibration - calibration of experimental data.

9.Approximation and interpolation of one-dimensional data.

10.Approximation and interpolation of multi-dimensional data.

11.Processing of selected data set with graphical output - individual work.

Study Objective:
Study materials:

Key References:

1.M. J. Crawley: The R Book WILEY, 2013

2.M. J. Crawley: Statistics: An Introduction Using R, WILEY, 2015

Recommended References:

3.A. Fiels, J. Miles, Z. Field: Discovering Statistics Using R, Sage Publications Ltd, 2012

Time-table for winter semester 2020/2021:
Time-table is not available yet
Time-table for summer semester 2020/2021:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2021-03-02
For updated information see http://bilakniha.cvut.cz/en/predmet5728806.html