SubjectsSubjects(version: 945)
Course, academic year 2012/2013
   Login via CAS
Presentation of Data and Data Analysis - MS710P26
Title: Prezentace výsledků a zpracování experimentálních dat
Guaranteed by: Institute of Applied Mathematics and Information Technologies (31-710)
Faculty: Faculty of Science
Actual: from 2012 to 2012
Semester: winter
E-Credits: 2
Examination process: winter s.:
Hours per week, examination: winter s.:0/2, C [HT]
Capacity: unlimited
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
State of the course: taught
Language: Czech
Explanation: Rozvrh po dohodě se zájemci - prosím kontaktuje vyučující na email
Additional information: http://www.karlin.mff.cuni.cz/~hudecova/education/
Note: enabled for web enrollment
Guarantor: RNDr. Šárka Hudecová, Ph.D.
Teacher(s): RNDr. Šárka Hudecová, Ph.D.
Opinion survey results   Examination dates   Schedule   
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).

 
Charles University | Information system of Charles University | http://www.cuni.cz/UKEN-329.html