SubjectsSubjects(version: 945)
Course, academic year 2023/2024
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Computational Environment for Statistical Data Analysis - NSTP004
Title: Výpočetní prostředí pro statistickou analýzu dat
Guaranteed by: Department of Probability and Mathematical Statistics (32-KPMS)
Faculty: Faculty of Mathematics and Physics
Actual: from 2018
Semester: winter
E-Credits: 6
Hours per week, examination: winter s.:2/2, C+Ex [HT]
Capacity: unlimited
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
State of the course: cancelled
Language: Czech, English
Teaching methods: full-time
Teaching methods: full-time
Guarantor: doc. RNDr. Zdeněk Hlávka, Ph.D.
Mgr. Pavel Schlesinger
Classification: Informatics > Software Applications
Mathematics > Probability and Statistics
Interchangeability : NMST440
Is incompatible with: NUOS002
Is interchangeable with: NUOS002
Annotation -
Last update: G_M (30.05.2011)
Writing mathematical texts (LaTeX, BibTeX, makeindex). Electronic journals and databases Zentralblatt and MathSciNet. The R system, functions and libraries, graphics, programming, simulations. Data management using programs R, awk and sed. Presentation of results: posters and slides in PDF. Using SAS for data management, statistical analysis and presentation of results. The course requires knowledge of basic statistical methods and programming.
Aim of the course -
Last update: T_KPMS (26.05.2008)

Detailed introduction to software suitable for statistical data analysis and presentation.

Literature - Czech
Last update: T_KPMS (14.05.2003)

Manuály k probíranému software

Teaching methods -
Last update: G_M (27.05.2008)

Lecture+exercises.

Syllabus -
Last update: G_M (30.05.2011)

1. HTML and some information sources on internet (JSTOR, Kluwer Online, Wiley Interscience, ScienceDirect, SpringerLink, Zentralblatt, MathSciNet).

2. LaTeX, BibTeX, makeindex, graphics.

3. The R system: data management. LATeX and HTML output, graphics, programming and simulations, functions and libraries.

4. Presentation of results, programs Scribus and Gimp, simple animations.

5. Simple data management using scripts and programs sed and awk.

6. Selected computationally intensive methods: EM algorithm, bootstrap etc.

7. Data analysis using SAS.

 
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