Course detail

Numerical and Statistical Treatment of Experimental Data

FCH-BC_ZDZAcad. year: 2023/2024

Basic statistical procedures and moderately advanced methods of processing of analytical data.
Principles of deskriptive statistics and basics of multivariate data analysis.

Language of instruction

Czech

Number of ECTS credits

3

Mode of study

Not applicable.

Guarantor

Mgr. Helena Doležalová Weissmannová, Ph.D.

Department

Institute of Chemistry and Technology of Environmental Protection (ÚCHTOŽP)

Entry knowledge

Basic knowledge of mathematical statistics and number of probabilities in the range of secondary education, ability to work in MS Excel environment.

Rules for evaluation and completion of the course

Successful passing the credit test and submitting a suitable seminar paper.
Attendance at lectures is recommended but not checked. For students of the combined form, consultations are organized in the scope of lectures for students of the day form of study.
Consultations are also provided to students of daily study on demand.
An integral part of the teaching and the combined form of teaching is the e-learning course, divided into blocks within which students are available to support learning, including electronic textbooks, presentations, lectures and other supplementary materials.

Aims

The aim is to deepen and doplninění knowledge in the field of statistics, methods of descriptive statistics, results processing and statistical analysis methods. Introduction to the specific processing of results in analytical chemistry, environmental chemistry.
Students will acquire new knowledge and experience in the following areas:
1. Basic statistical methods of experimental data analysis, basic statistical analysis of analysis results.
2. Method of numerical processing of one-dimensional data, basics of descriptive statistics
3. Testing statistical hypotheses
4. Confirmatory analysis methods (eg confidence intervals, regression analysis, etc.) and exploration analyzes (eg cluster analysis, exploration factor analysis, PCA,
5. Fundamentals of multidimensional analysis
6. Practical use of acquired knowledge in processing of experimental data from selected thematic circuits in MS-Excel environment. or Statistics

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

M. Meloun, J. Militký, Kompendium statistického zpracování dat. Academia 2001, ISBN 80-200-1008-4
Doležalová: Studijní opory.https://www.vutbr.cz/elearning/course (CS)
Hebák,P., Hustopecký, J. et al.:Vícerozměrné statistické metody. Praha: Informatorium, 2004. (CS)
J. Pavlík a kol., Aplikovaná statistika, VŠCHT Praha, 2005, ISBN 80-7080-569-2
Miller J.N., Miller J.C.: Statistics and Chemometrics for Analytical Chemistry. Pearson, Harlow 2005 (CS)
Richard C. Graham: Data Analysis for the Chemical Sciences. VCH Publishers, Inc., New York, 1993, ISBN 1-56081-048-3 (CS)

Recommended reading

Not applicable.

Elearning

eLearning: currently opened course

Classification of course in study plans

  • Programme BPCP_ECHBM Bachelor's 3 year of study, summer semester, compulsory-optional
  • Programme BKCP_ECHBM Bachelor's 3 year of study, summer semester, compulsory-optional

Type of course unit

 

Seminar

26 hod., compulsory

Teacher / Lecturer

Mgr. Helena Doležalová Weissmannová, Ph.D.

Syllabus

1. Introduction, basic concepts, results errors.
2. Descriptive characteristics of statistical files (arithmetic mean, modus, median, range, variance, standard deviation, variation coefficient)
3. Probability and statistics.
4. Basics of statistical induction - Point estimation, Interval estimation, Statistical hypothesis tests, parametric and nonparametric tests (one-sampleT-test, two-sample T-test, paired T-test, nonparametric tests).
5. Regression analysis. Regression models. Regression functions. Linear regression functions of interpreting their parameters.
6. Correlation analysis: principles, correlation models, correlation coefficients.
7. Multivariate Statistical Methods - Factor Analysis (FA), Cluster Analysis, Discrimination Analysis (DA), Correspondence Analysis (CA), Principal Component Analysis (PCA).
8. Summary of Application of Multidimensional Methods in Data Analysis.
9. Practical use of acquired knowledge in processing of experimental data using MS-Excel, Statistics.
10. Working with MS-Excel, SW Statistica - basics
11. Descriptive statistics - SW Statistica
12. Regression and correlation analysis - SW Statistica
13. Examples of multivariate analysis - SW Statistica

Elearning

eLearning: currently opened course