Duration
Part 1 : Descriptive Statistics : 16h Th, 8h Pr, 8h Mon. WS
Introduction to probability : 9h Th, 7h Pr, 2h Mon. WS
Number of credits
| Bachelor in mathematics | 5 crédits |
Lecturer
Part 1 : Descriptive Statistics : Gentiane Haesbroeck
Introduction to probability : Gentiane Haesbroeck
Coordinator
Language(s) of instruction
French language
Organisation and examination
Teaching in the second semester
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
Look at the contents of the two parts.
Part 1 : Descriptive Statistics
Basic concepts of descriptive statistics are taught in this course. More precisely, here follows the content of the course:
- Statistical tables and graphics
- Summary statistics (central tendency, dispersion and shape)
- Correlation analysis and linear regression
The learning of a statistical software is also included in the material of the course.
Introduction to probability
This partim 2 is dedicated to a complement of descriptive statistics (M-estimation, geometric, harmonic and generalized means, Lorenz curve and Gini index) and to an introduction to probability theory.
Learning outcomes of the learning unit
Look at the learning outcomes of the two partims.
Part 1 : Descriptive Statistics
After this course, the student should be able to present data appropriately, compute appropriate parameters in order to analyse data and interpret the results.
Introduction to probability
After this part, the student should be able to formalize the estimation process for the centre of a distribution, to adpat the definition of the mean to the context and to model the distribution of the wages of a population in order to compute a measure of inequality.
Additionaly, the student should be able to use correctly probability calculus.
Prerequisite knowledge and skills
look at the prerequisites for each part.
Part 1 : Descriptive Statistics
basic concepts of analysis and algebra taught in secondary school.
Introduction to probability
A good knowledge of set theory and of the classical formulae of combinatorial analysis is necessary (these specific matters are taught in the course entitlde 'elementary mathematics', Bloc 1, first semester)
Planned learning activities and teaching methods
Look at the information given for each part.
Part 1 : Descriptive Statistics
The course is divided into three parts:
- Theory
- Exercises
- Learning of a statistical software
The type of teaching for the theory part is ex-cathedra. The professor uses beamer projections or writes on the black boards. When slides are used, these will be available in advance on eCampus.
The tutorials combine ex-cathedra presentation and individual work for the students. It is recommended to check the good knowledge of the content of the course via the on-line QCMs.
The statistical software will be taught differently in the two groups:
In Bachelor of mathematics:
Two practicals organised in the computer room of the mathematics department will be organised in order for the students to get the bacis of the statistical software.
In Bachelier of computer sciences:
The statistical software will be taught by means of an auto-learning process using documents on-line on eCampus.
Introduction to probability
The learning activites are similar to those presentedin the first part, except that there is no statistical software activities in this second part.
Mode of delivery (face-to-face ; distance-learning)
look at the information indicated in the partims.
Part 1 : Descriptive Statistics
The courses and the tutorials are given face-to-face over the second semester according to a timetable distributed to the students in the beginning of the academic year.
The theory lectures are recorded by means of the podcast equipment installed in the lecture room. The students may visualize these recordings when they want.
Introduction to probability
The delivery mode is again face-to-face. If required by the students, the recording of the lectures by means of the podcast equipment might be continued in this second part.
Recommended or required readings
Look at the informations given in the partims.
Part 1 : Descriptive Statistics
Notes written in French (on the theory and on the exercises) will be sold to the students at the start of the academic year. These notes will also be available on line (via eCampus)
Introduction to probability
Notes written in French (on the theory and on the exercises related to the contents of the second part) will be sold to the students and also put on line.
Assessment methods and criteria
The final mark is based on the marks attributed to the two following assessments (all taking place in May-June):
- written exam (theory and exercises) jointly on the contents from the two partims
- practical exam on descriptive statistics in the computer room
The computation of the final grade will be specified during the first lecture. The computation will be different whether the two separate marks are bigger or equal to 5/20 or not. In case at least one of the two marks is below 5/20, the global mark will not exceed 9/20.
In case of absence at one part of the exam, the students will be given a mark of 0/20 for that part.
Part 1 : Descriptive Statistics
Bachelor in mathematics: look at the information given for the global course.
Bachelor in computer sciences: the final mark is a weighted mean based on the marks attributed to the two following assessments :
- written exam on theory, exercises and interpretation of software outputs
- practical exam in the computer room
The details of the computation of the final grade will be specified at the end of the course. In case of a partial mark below 5/20, the global mark will not exceed 9/20.
Introduction to probability
look at the general information.
Work placement(s)
Organizational remarks
None
Part 1 : Descriptive Statistics
None
Introduction to probability
None
Contacts
G.HAESBROECK, Institute of mathematics, Building B37, room 0/60, tel: 04/366-95-94, email: G.Haesbroeck@ulg.ac.be M. ERNST, Institute of mathematics, Building B37, email: m.ernst@ulg.ac.be
Part 1 : Descriptive Statistics
The details are given above.
Introduction to probability
The details are given above.