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Application of Statistics in Research Process - NMST564

Title:	Aplikace statistiky ve výzkumném procesu
Guaranteed by:	Department of Probability and Mathematical Statistics (32-KPMS)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2023
Semester:	summer
E-Credits:	1
Hours per week, examination:	summer s.:0/1, C [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	taught
Language:	Czech
Teaching methods:	full-time
Teaching methods:	full-time

Guarantor:	Mgr. Martin Otava, Ph.D.
Class:	M Mgr. PMSE M Mgr. PMSE > Volitelné
Classification:	Mathematics > Probability and Statistics
Pre-requisite :	NMSA407

Opinion survey results Examination dates SS schedule Noticeboard

Annotation -

Last update: doc. Ing. Marek Omelka, Ph.D. (23.05.2022)

The application of the statistical methods in research problems.

Aim of the course -

Last update: RNDr. Jitka Zichová, Dr. (10.06.2022)

Prepare students for a complex role of a statistician in a research team.

Course completion requirements -

Last update: RNDr. Jitka Zichová, Dr. (10.06.2022)

Handling of two assignments of sufficient quality. The nature of these requirements precludes any possibility of additional attempts to obtain the exercise class credit.

Literature -

Last update: doc. Ing. Marek Omelka, Ph.D. (23.05.2022)

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition" (Springer Series in Statistics) 2nd (2009) Corrected Edition by Hastie Trevor, Tibshirani Robert & Friedman Jerome

Design and Analysis of Experiments (fifth edition), Douglas Montgomery, John Wiley and Sons, 2001, 684 pages

Bayesian Methods in Pharmaceutical Research, Emmanuel Lesaffre, Gianluca Baio,Bruno Boulanger, 2020, Chapman and Hall/CRC

Teaching methods -

Last update: doc. Ing. Marek Omelka, Ph.D. (23.05.2022)

Seminar.

Syllabus -

Last update: RNDr. Jitka Zichová, Dr. (10.06.2022)

1. Basic concept of a role of statistician in research team. Data collection, representative population, real world data.

2. Designing experiments in practice: screening, augmentation and response surface areas. Practical complications and their methodological solutions.

3. Variability in population vs variability in the measuring system: variance modelling.

4. Equivalence tests: proving homogeneity.

5. Probability statement as an output of analysis: easy interpretation of Bayesian methods

6. Data visualization.