SubjectsSubjects(version: 953)
Course, academic year 2023/2024
Statistical treatment of experimental data - AP403015
Title: Statistical treatment of experimental data
Guaranteed by: Department of Physical Chemistry (403)
Faculty: Faculty of Chemical Engineering
Actual: from 2019
Semester: both
Points: 0
E-Credits: 0
Examination process:
Hours per week, examination: 2/1, other [HT]
Capacity: winter:unknown / unknown (unknown)
summer:unknown / unknown (unknown)
Min. number of students: unlimited
State of the course: taught
Language: English
Teaching methods: full-time
Teaching methods: full-time
Note: course is intended for doctoral students only
can be fulfilled in the future
you can enroll for the course in winter and in summer semester
Guarantor: Matějka Pavel prof. Dr. RNDr.
Dendisová Marcela Ing. Ph.D.
Interchangeability : P403015
This subject contains the following additional online materials
Annotation -
The subject represents an introductory course of the mentioned topic for Ph.D. students. The following chapters are discussed: Principles and advances of probability theory, various types of distributions and their parameters, point and interval estimates, linear and nonlinear regression, hypothesis testing. All the theory is supplemented with examples of experimental data processing. Doctoral students' own data (suitable for statistical processing) are welcome to practice.
Last update: Matějka Pavel (31.08.2019)
Aim of the course -

Students will be able to:

  • describe the principles of statistical treatment of experimental data focused on large data files obtained by multiple experimental techniques
  • apply the above-mentioned principles on real experimental data emphasizing the solution of physicochemical issues and the use of advanced analytical methods
  • evaluate the relevancy of outputs of statistical method and to eliminate wrong results and computational artifacts
Last update: Matějka Pavel (31.08.2019)
Literature -

R: Statistical Methods in Analytical Chemistry, Second Edition, PETER C. MEIER, RICHARD E. ZUND, 2000 by John Wiley & Sons, Inc., ISBN 0-47 1-29363-6, Online ISBN:9780471728412 |DOI:10.1002/0471728411

R: Chemometrics: Data Driven Extraction for Science, Second Edition. Richard G. Brereton, 2018 by John Wiley & Sons Ltd., ISBN:9781118904664 |Online ISBN:9781118904695 |DOI:10.1002/9781118904695

A: Chemometrics: Statistics and Computer Application in Analytical Chemistry, Third Edition, Author: Matthias Otto, 2017 Wiley‐VCH Verlag GmbH & Co. KGaA Print ISBN:9783527340972 |Online ISBN:9783527699377 |DOI:10.1002/9783527699377

A: Published review papers and original studies.

Last update: Matějka Pavel (31.08.2019)
Learning resources -

electronic materials and files available on-line in the domain

Last update: Pátková Vlasta (16.11.2018)
Syllabus -

1) Experimental data and their compatibility, preparation of data input for statistical treatment

2) Variability of experimental data, sources of errors and uncertainties and their propagation

3) Exploratory analysis of vector data — principles

4) Exploratory analysis of vector data — applications and tasks

5) Statistical analysis of vector data — principles

6) Statistical analysis vector data — applications and tasks

7) Statistical analysis of multidimensional data — principles

8) Statistical analysis of multidimensional data — applications and tasks

9) Analysis of variance — principles

10) Analysis of variance — applications and tasks

11) Regression methods — linear and nonlinear — principles

12) Regression methods — linear and nonlinear — applications and tasks

13) Correlation — principles and tasks

14) Interpolation and approximation — principles and tasks

Last update: Matějka Pavel (31.08.2019)