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.
7 credits
45.0 h + 40.0 h
Q1
Teacher(s)
Legrand Catherine;
Language
French
Prerequisites
The prerequisite(s) for this Teaching Unit (Unité d’enseignement – UE) for the programmes/courses that offer this Teaching Unit are specified at the end of this sheet.
Main themes
- Introduction to probability ; discrete (binomiale, multinomial and Poisson) and continuous (normal, chi-square, Student and Fisher-Snedecor) distributions. - Descriptive statistics (measures of location and dispersion, empirical distribution, histograms, graphs, dependence measures and their graphical representations) - Introduction to statistical inference: point estimation, confidence intervals, hypothesis tests ; application to the comparison of means and variances. - ANOVA I and ANOVA II models. - Linear models : linear and multiple regression. - Simple, partial and multiple correlations. - Inference methods for discrete data and contigency tables. - Introduction to the planning of experiments.
Aims
At the end of this learning unit, the student is able to : | |
| 1 |
The goal of that course is to introduce students in veterinary science to the rational use of statistical methods for the analysis of data in their discipline. |
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”.
Teaching methods
An introduction to a data analysis software will be proposed during the practicals.
Evaluation methods
The evaluation includes a theoretical part and a practical part as well as project.
Other information
Prerequisites: Basic courses in mathematics (PHY1114 - PHY1115 or equivalent).
Teaching materials
- matériel sur moodle
Faculty or entity
VETE
