Statistical computing

lstat2020  2018-2019  Louvain-la-Neuve

Statistical computing
6 credits
20.0 h + 20.0 h
Q1
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


Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Mathematics

Master [120] in Actuarial Science

Master [120] in Agricultural Bioengineering

Master [120] in Chemistry and Bioindustries

Master [120] in Statistic: Biostatistics

Master [120] in Environmental Bioengineering

Master [120] in Forests and Natural Areas Engineering

Master [120] in Biomedical Engineering

Master [120] in Statistic: General

Master [120] in Mathematical Engineering

Master [120] in data Science: Statistic

Minor in Statistics and data sciences