SubjectsSubjects(version: 953)
Course, academic year 2023/2024
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Experimental Design and Multivariate Statistical Analysis - MC230P58
Title: Plánování experimentů a predikční vícerozměrná analýza
Czech title: Plánování experimentů a predikční vícerozměrná analýza
Guaranteed by: Faculty of Matematics and Physics, CU (31-MFF)
Faculty: Faculty of Science
Actual: from 2020
Semester: summer
E-Credits: 3
Examination process: summer s.:
Hours per week, examination: summer s.:0/3, C [HT]
Capacity: unlimited
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
State of the course: not taught
Language: Czech
Explanation: v LS 2020/21 nejistota s koronavirem, nevyučován
Note: enabled for web enrollment
Guarantor: RNDr. Jitka Zichová, Dr.
Is interchangeable with: NMST705
Opinion survey results   Examination dates   Schedule   
Annotation -
The course is dedicated to some methods of data analysis which enable to obtain as much as possible information from large amounts of measurements in medicine, pharmaceutical or environmental research etc. The aim is to show the practical use of the methods, therefore the analysis of real data on computer using an appropriate statistical software is an important part of the course. Two types of problems are solved: investigating the influence of several factors on a variable by means of experimental design and classification of a large data set by means of multivariate statistical analysis. Regression and time series models are also explained and applied to data.
Last update: ZICHOVA/NATUR.CUNI.CZ (20.05.2008)
Aim of the course -

To demonstrate selected methods of statistical data analysis and their application to experimantal material.

Last update: ZICHOVA/NATUR.CUNI.CZ (20.05.2008)
Literature - Czech

Zichová, J.: Plánování experimentů a predikční vícerozměrná analýza. Karolinum, Praha, 2007.

Last update: Zichová Jitka, RNDr., Dr. (07.04.2016)
Requirements to the exam -

Active presence at the seminars and solving a project consisiting in a real data analysis.

Last update: Zichová Jitka, RNDr., Dr. (11.11.2011)
Syllabus -

1. Basic probability and statistics - random events and variables, normal distribution, random sample, parameter estimating, testing of statistical hypotheses.

2. Linear and logistic regression - description of a continuous or a binary variable using a set of regressors.

3. Testing hypotheses concerning the mean - t-tests and their non-parametric alternatives.

4. Analysis of variance - one-way and two-way ANOVA, hierarchical model, latin squares.

5. Factorial experimental design - two-level factorial design, fractional factorial design.

6. Principal component analysis - interpretation of the principal components in data sets.

7. Cluster analysis - hierarchical and non-hierarchical clustering algorithms.

8. Discriminant analysis - classification of objects to a given set of groups.

9. Time series analysis - modelling and forecasting.

Last update: ZICHOVA/NATUR.CUNI.CZ (20.05.2008)
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