Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
4 credits
15.0 h + 5.0 h
Q2
Teacher(s)
Kestemont Marie-Paule;
Language
French
Main themes
Topics to be treated
- General framework of inference in finite population; population, sampling, statistics for the inference based on experimental data, linear homogenous estimation: elementary units, complex units.
- Sampling with unequal probabilities: Hansen-Hurwitz and Horvitz-Thompson estimators, for the particular case of simple random sampling.
- Estimators improvement through auxiliary information: ratio estimator, regression estimator
- Sampling from complex units: stratified sampling, cluster sampling, two stages sampling.
- Sampling from biological populations: basic issues in sampling, estimation of the population size.
Aims
At the end of this learning unit, the student is able to : | |
| 1 |
Objective (in terms of abilities and knowledge) This course aims at providing the student the basic knowledges on the sampling methods, with a particular, but not exclusive, emphasis on sampling from (finite) human populations. At the end of the course, the student should be able to correctly designing a simple survey and analysing the results. |
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
General framework of inference in finite population :
- Techniques of random samplings and estimators properties.
- Simple random sampling
- Stratified random sampling
- Uneven probability sampling
- Cluster sampling
- Multi-level sampling
Teaching methods
8 x 2 hours of masterful presentations and 2 x 2 hours of practical exercices on computer.
Evaluation methods
Written examination in session : 14 points on 20.
Individual project delivered for the beginning of the first session : 6 points on 20.
Individual project delivered for the beginning of the first session : 6 points on 20.
Online resources
MOODLEUCL : lecture LSTAT2200.
Bibliography
Tillé, Y. (2001). Théorie des sondages : échantillonnage et estimation en populations finies, (Cours et exercices avec solutions), Dunod, Paris.
Sharon Lohr (1999), Sampling : Design and Analysis, Duxbury Press Rao Poduri S.R.S. (2000), Sampling Methodologies with Applications, London : Chapman and Hall.
Teaching materials
- transparents sur moodle
Faculty or entity
LSBA
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
Mineure en statistique et science des données
Approfondissement en statistique et sciences des données
Minor in Statistics, Actuarial Sciences and Data Sciences
Master [120] in Data Science : Statistic
Master [120] in Data Science Engineering
Certificat d'université : Statistique et sciences des données (15/30 crédits)
Master [120] in Data Science: Information Technology
Master [120] in Economics: General
Master [120] in Statistic: General