SubjectsSubjects(version: 861)
Course, academic year 2019/2020
  
Presentation of Data and Data Analysis - MS710P26
Title: Prezentace výsledků a zpracování experimentálních dat
Czech title: Prezentace výsledků a zpracování experimentálních dat
Guaranteed by: Institute of Applied Mathematics and Information Technologies (31-710)
Faculty: Faculty of Science
Actual: from 2017
Semester: winter
E-Credits: 2
Examination process: winter s.:
Hours per week, examination: winter s.:0/2 C [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: not taught
Language: Czech
Additional information: http://www.karlin.mff.cuni.cz/~hudecova/education/
Guarantor: RNDr. Šárka Hudecová, Ph.D.
Annotation -
Last update: ZICHOVA/NATUR.CUNI.CZ (20.05.2008)
Selected principles of statistical inference. The analysis of real data on computer using an appropriate statistical software is an important part of the course. The attention is devoted to correlation and regression models, analysis of variance, some algorithms of cluster analysis and fundamentals of time series analysis.
Aim of the course -
Last update: ZICHOVA/NATUR.CUNI.CZ (20.05.2008)

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

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

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

Requirements to the exam -
Last update: RNDr. Jitka Zichová, Dr. (11.11.2011)

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

Syllabus -
Last update: RNDr. Šárka Hudecová, Ph.D. (14.09.2012)

1. Basic concepts of mathematical statistics, principles of testing hypotheses.

2. Testing hypotheses concerning the mean - t-tests (one-sample, two-samples, paired) and their non-parametric alternatives.

3. Analysis of variance - one-way and two-way ANOVA.

4. Correlation, linear regression, independence testing in contingency tables.

5. Multivariate statistics (cluster analysis, principal components, discimination analysis).

 
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