Course detail

Numerical and Statistical Treatment of Experimental Data II

FCH-MCA_ZED2Acad. year: 2008/2009

Basic methods of numerical treatment of one-dimensional data, numeric treatment of simple dependencies, data smoothing, derivation, integration, combinated dependency deconvolution, mathematic statistics for chemists - one-dimensional data, functional dependencies, planning of experiments, optimizational computation.

Language of instruction

Czech

Number of ECTS credits

2

Mode of study

Not applicable.

Guarantor

RNDr. Jaroslav Mega, Ph.D.

Department

Institute of Chemistry and Technology of Environmental Protection (ÚCHTOŽP)

Learning outcomes of the course unit

Integral information on fundamental methods of numerical treatment
of one-dimensional data, fundamental methods of numerical treatment of
simple dependences and data smoothing.
Practical utilization of acquired piece of knowelge in experimental
data processing from selected thematic fields in MS-Excel environment.

Prerequisites

Knowledge from courses Hydrochemistry, Technology of water treatment, Laboratory classes - technology of water treatment I; capability to work in environment of MS Excel

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.

Assesment methods and criteria linked to learning outcomes

Graded course-unit credit
Requirements:
- successful passing of credit test
- oral examination by colloquial form

Course curriculum

1. Chemical data
2. Probability and statistics. Probability distributions
3. Point estimators, confidence intervals, significance levels and hypotesis tests
4. Tests for comparison of means
5. Transformation of instrumental data
6. Design of experiments
7. Analysis of variance (ANOVA)
8. Linear models
9. Quantification of analytes. Mesure of performance of analytical methods
10. Mesurement of data quality
11. Non-parametric tests
12. Numerical treatment of data
13. Multiple regression

Work placements

Not applicable.

Aims

Replenishment and acquisition of new theoretical information and practical know-how necessary for numerical processing of data acquired by own experimental activities.

Specification of controlled education, way of implementation and compensation for absences

none

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Zdeněk Šťastný: Matematické a statistické výpočtu v Microsoft® Excelu. Computer Press Brno 1999, ISBN 80-7226-141-X (CS)
Milan Meloun a Jiří Militký: Kompendium statistického zpracování dat. Academia 2002 (CS)

Recommended reading

Jerald L. Schnoor: Environmental Modeling. John Wiley a Sons, Inc., New York 1996, ISBN 0-471-12436-2 (CS)
Richard C. Graham: Data Analysis for the Chemical Sciences. VCH Publishers, Inc., New York, 1993, ISBN 1-56081-048-3 (CS)
Billo, E. J.: MS Excel for Chemists. John Wiley & Sons, Inc., New York 1997 (CS)
Green, J. R. a Margerison, D.: Statistical treatment of experimental data. Elsevier Sci. Publ. Comp. Amsterodam 1978 (CS)
F. Carley and P. H. Morgan: Computaional Methods in the Chemical Sciences. Ellis Horwood Limited Chichester 1989, ISBN 0-85312-746-8 (CS)

Classification of course in study plans

  • Programme NPCP_CHTOZP Master's

    branch NPCO_CHTOZP , 2. year of study, winter semester, compulsory-optional

  • Programme NKCP_CHTOZP Master's

    branch NKCO_CHTOZP , 2. year of study, winter semester, compulsory-optional

  • Programme CKCP_CZV lifelong learning

    branch CKCO_CZV , 1. year of study, winter semester, compulsory-optional

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

RNDr. Jaroslav Mega, Ph.D.