At the end of the course, the student should be able to • to carry out a descriptive statistic and • to pose a hypothesis test and solve it, in order to interpret the reality hidden by the data set, • to carry out a statistical estimate, a linear regression, • to use the R language wisely to manipulate its data, • use the foundations of combinatorial analysis and probability calculus to determine the probabilities associated with different events, • manipulate the usual theoretical laws to explain behaviour and carry out simulations using the R language, • manipulate the laws associated with random variables studied simultaneously.
The aim here is to provide the student with a deep and accurate understanding of the fundamental concepts as well as training in probabilistic and statistical reasoning. The mathematical formalism is simplified but present. The aim is to use the theory of measurement in an intuitive way to extend the concept of enumeration towards an analytical definition of probability laws. The basic subject of probability calculation is introduced by a few hours of descriptive statistics (treatment of a table of numbers, calculation of average, variance,...) which give rise to practical exercises with R. The principles of probability necessary for an introduction to inferential statistics are also defined. The course will focus on the basic techniques of linear regression, parameter estimation and hypothesis testing.
The final mark out of 20 is obtained after an individual written examination.
This course is based on various basic works in statistics and probability, and in particular on F. Bertrand and M. Maumy-Bertrand. Bertrand and M. Maumy-Bertrand.Initiation à la statistique avec R. Dunod, 2010, chapters 1 to 8 by S.M. Ross. Initiation aux probabilités. Translation of the seventh American edition. Presses polytechniques et universitaires romandes, 2009 and finally, on the following book: M. Lejeune. Statistique. The theory and its applications. Second edition. Springer, 2010.
| Training | Study programme | Block | Credits | Mandatory |
|---|---|---|---|---|
| Bachelor in Computer Science (Evenings and Weekends Schedule) | Standard | 0 | 10 | |
| Bachelor in Computer Science (Evenings and Weekends Schedule) | Standard | 2 | 10 |