Due to the COVID-19 crisis, the information below is subject to change,
in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
4 credits
25.0 h + 25.0 h
Q2
This learning unit is not being organized during year 2020-2021.
Teacher(s)
Schtickzelle Nicolas;
Language
French
Aims
At the end of this learning unit, the student is able to : | |
| 1 | The student shall understand, and become able to use correctly and critically the principal methods for the statistical analysis of biological and environmental univariate data. He perceives the relationship between experimental design and analysis model and the necessity of planning experiments, and becomes familiar with computer-aided data analysis. After completing this course, the student should master the basic methods for the analysis of univariate data, be able to choose the analysis model and method adapted to the design of simple factorial experiments, to analyse and interpret correctly the results of such experiments. He should be able to progress by himself and follow fruitfully advanced lectures on experimental design and data analysis. |
Content
With this course, the student acquires the basic notions and principles of probabilities and statistical inference necessary for the scientific process. At the end of the learning phase, they are able to determine the important characteristics of an experimental design, to select and carry out the appropriate statistical analysis for the analysis of the data, and to interpret the results and possible limitations to the conclusions to be drawn.
The course begins with the basics of probability theory. It then details the principles of statistical inference (population vs sample, variables and distributions, sources of variations in the data, hypothesis testing, p-value and type I and II error, confidence interval ...). The main types of basic statistical analysis are detailed and illustrated: t test, ANOVA (1, 2 and 3), correlation and simple linear regression, count data (X²). The principles of permutation tests are also discussed.
The course is complemented by practical work on computer using the software R, which allow the student to carry out in practice all the statistical analyzes discussed.
The course begins with the basics of probability theory. It then details the principles of statistical inference (population vs sample, variables and distributions, sources of variations in the data, hypothesis testing, p-value and type I and II error, confidence interval ...). The main types of basic statistical analysis are detailed and illustrated: t test, ANOVA (1, 2 and 3), correlation and simple linear regression, count data (X²). The principles of permutation tests are also discussed.
The course is complemented by practical work on computer using the software R, which allow the student to carry out in practice all the statistical analyzes discussed.
Teaching methods
Due to the COVID-19 crisis, the information in this section is particularly likely to change.
Audience course and practical work in a computer room. The student is encouraged to interactivity for all these activities.
Evaluation methods
Due to the COVID-19 crisis, the information in this section is particularly likely to change.
Open-book written exam for theoretical comprehension of concepts, and open-book practical exam with computer-based R software for the realization and interpretation of statistical analyzes on real datasets.
Other information
A basic knowledge of the R software is required: the student is expected to be able to create and modify R-data sets independently. The course LBIO1282 aims specifically to give the student this knowledge; if he has not followed it beforehand, the student must be trained autonomously in these skills, eg by means of the many resources available online for free.
Online resources
Course slides and materials for practical work are available on Moodle.
Teaching materials
- Transparents sur Moodle
Faculty or entity
BIOL
