UCL - Statistical computing

[STAT2020]

[20h + 20h] 6 credits

This course is taught in the 1st semester.

Teacher(s): Céline Bugli(supplée Bernadette Govaerts), Bernadette Govaerts

Language: French

Level: Second

Aims
Main themes
Content and teaching methods
Other information (prerequisite, evaluation (assessment methods), course materials recommended readings, ...)

Aims

At the end of this course, the students will have gain a critical view of the different classes of statistical software available on the market and basic culture on statistical algorithms and graphics. They will also be able to realise basic statistical analysis with different software (SAS, S-Plus, R, Excel, SPSS...) and write programs in the S and SAS programming languages.

This course is organised in two parts:
Part (A): basics of statistical computing and case studies.
Part (B): statistical algorithms and SAS and R Software

Main themes

Main themes:

Part (A):
- Steps of a statistical data analysis with a statistical software
- Classes of statistical software
- Statistical graphics: main classes of graphics and efficient use - Basic statistical analysis with "point and click" statistical software.

Part (B):
- Random numbers generation, calculation of probabilities and quantiles for most common statistical distributions.
- Algorithms to estimate linear and non linear models and associated numerical problems.
- Maximum likelihood estimation.
- Introduction to resampling methods
- Programming in the S language under the S-Plus or R environment.
- Programming in SAS (Use of SAS/BASE, SAS/STAT and SAS/Graph).

Content and teaching methods

Part (A):
- Steps of a statistical data analysis with a statistical software
- Classes of statistical software
- Statistical graphics: main classes of graphics and efficient use - Basic statistical analysis with "point and click" statistical software.

Part (B):
- Random numbers generation, calculation of probabilities and quantiles for most common statistical distributions.
- Algorithms to estimate linear and non linear models and associated numerical problems.
- Maximum likelihood estimation.
- Introduction to resampling methods
- Programming in the S language under the S-Plus or R environment.
- Programming in SAS (Use of SAS/BASE, SAS/STAT and SAS/Graph).

Other information (prerequisite, evaluation (assessment methods), course materials recommended readings, ...)

Prerequisites
Basic course in statistics and capability to work on a personnel computer.

Teaching materials
See web site : www.stat.ucl.ac.be/cours/stat2430

For more information:

http://www.stat.ucl.ac.be/cours/stat2430/http://www.uclouvain.be/index.html