Statistical computing

Teacher(s)

Bugli Céline (compensates Govaerts Bernadette); Govaerts Bernadette;

Language

French

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).

Aims

At the end of this learning unit, the student is able to :
1	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

The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.

Content

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).

Faculty or entity

LSBA