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.
5 credits
24.0 h + 36.0 h
Q1
Teacher(s)
Segers Johan; SOMEBODY;
Language
French
Prerequisites
In order to successfully follow this course, you should be acquainted with the concept of Probability and the
rules of Probability calculus, the bases of statistical inference, the principles and practice of the classical methods
for statistical analysis of continuous data (Regression, Analysis of Variance) and of discrete data (Contingency
tables, Goodness of fit tests), and the use of a statistical software for applying the above.
rules of Probability calculus, the bases of statistical inference, the principles and practice of the classical methods
for statistical analysis of continuous data (Regression, Analysis of Variance) and of discrete data (Contingency
tables, Goodness of fit tests), and the use of a statistical software for applying the above.
Main themes
Taking into account the most frequently encountered needs of researchers in Biology, as well as the time
constraints, the course offers of two main modules : Linear Modeling, and Methods of Multivariate Analysis.
The examples presented are mainly drawn from researches in Ecology.
constraints, the course offers of two main modules : Linear Modeling, and Methods of Multivariate Analysis.
The examples presented are mainly drawn from researches in Ecology.
Aims
At the end of this learning unit, the student is able to : | |
| 1 | The objectives are that, as a result of successfully attending this course, the students : ' Are aware of the necessity of planning any scientific experiment before it is started. ' Have practiced, in the frame of a personal scientific question, the principles of experimental design. ' Are able to review, choose, and apply knowingly the best adapted methods for modeling and analysing data from their domain of expertise in Biology. ' Are able to set up a scientific experiment, to manage the data generated by this experiment, to analyse them (usually with the help of a computer software), and to interprete critically the results. ' Have shown their ability to report a scientific experiment in a written document and through an oral communication. These reports may be elaborated in groups of two or three students. |
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
Module 1 (UCLouvain): Linear statistical modeling
– Simple and multiple linear regression, AN(C)OVA included
– Generalised linear models: logistic and Poisson regressionoisson
– Linear mixed models
– Implementation in R
Module 2 (UNamur): Multivariate data exploration
– Data matrices
– Useful techniques from matrix algebra
– Multiple linear regression (no inference)
– Principal component analysis
– Classification
– Canonical correspondence analysis
– Implementation in R and Excel
– Simple and multiple linear regression, AN(C)OVA included
– Generalised linear models: logistic and Poisson regressionoisson
– Linear mixed models
– Implementation in R
Module 2 (UNamur): Multivariate data exploration
– Data matrices
– Useful techniques from matrix algebra
– Multiple linear regression (no inference)
– Principal component analysis
– Classification
– Canonical correspondence analysis
– Implementation in R and Excel
Teaching methods
Lectures and exercise classes in computer rooms.
For Module 2 (UNamur), self-learning sessions and flipped classrooms; instructions will be given in the first course hour.
For Module 2 (UNamur), self-learning sessions and flipped classrooms; instructions will be given in the first course hour.
Evaluation methods
Each of the two teachers will give a grade on 10 and this will grade will count for 50 % in the total. To succeed, the sum of the two grades should at least be 10/20 and each grade should at least be 4/10. Partial grades of 5/10 and higher are credited for the running academic year.
Module 1 (UCLouvain) : written exam during the exam session. Dispensatory test for a part of the exam near the end of the lectures.
Module 2 (Unamur) : Continuous evaluation during flipped classrooms (50%) : multivariate analyses in Excel and interpretation of the results. Evaluation during exercise classes (50%) : multivariate data analyses in R and interpretation of the results. No second session.
Module 1 (UCLouvain) : written exam during the exam session. Dispensatory test for a part of the exam near the end of the lectures.
Module 2 (Unamur) : Continuous evaluation during flipped classrooms (50%) : multivariate analyses in Excel and interpretation of the results. Evaluation during exercise classes (50%) : multivariate data analyses in R and interpretation of the results. No second session.
Online resources
Moodle page: https://moodleucl.uclouvain.be/course/view.php?id=7525
Module 1 (UCLouvain): R scripts of the recommended book: http://highstat.com/index.php/analysing-ecological-data
Module 2 (UNamur)
– Self-study website: http://webapps.fundp.ac.be/umdb/biostats2017/
– Videos:
http://medias.save.fundp.ac.be/videos/webcampus/2016-cours-biostatistique-Depiereux/module-200-10.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2016-cours-biostatistique-Depiereux/module-210-10.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2016-cours-biostatistique-Depiereux/module-220-10.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2016-cours-biostatistique-Depiereux/module-220-20.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2016-cours-biostatistique-Depiereux/module-220-30.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2016-cours-biostatistique-Depiereux/module-230-10.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2017-cours-biostatistique-Depiereux/module-240-10.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2017-cours-biostatistique-Depiereux/module-240-20.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2017-cours-biostatistique-Depiereux/module-240-30.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2017-cours-biostatistique-Depiereux/module-240-40.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2017-cours-biostatistique-Depiereux/module-240-50.mp4
Module 1 (UCLouvain): R scripts of the recommended book: http://highstat.com/index.php/analysing-ecological-data
Module 2 (UNamur)
– Self-study website: http://webapps.fundp.ac.be/umdb/biostats2017/
– Videos:
http://medias.save.fundp.ac.be/videos/webcampus/2016-cours-biostatistique-Depiereux/module-200-10.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2016-cours-biostatistique-Depiereux/module-210-10.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2016-cours-biostatistique-Depiereux/module-220-10.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2016-cours-biostatistique-Depiereux/module-220-20.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2016-cours-biostatistique-Depiereux/module-220-30.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2016-cours-biostatistique-Depiereux/module-230-10.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2017-cours-biostatistique-Depiereux/module-240-10.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2017-cours-biostatistique-Depiereux/module-240-20.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2017-cours-biostatistique-Depiereux/module-240-30.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2017-cours-biostatistique-Depiereux/module-240-40.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2017-cours-biostatistique-Depiereux/module-240-50.mp4
Bibliography
- Dias cours magistraux, syllabus TP, bases de données, codes informatiques. Site web auto-apprentissage.
- Alain F. Zuur, Elena N. Iono, Graham M. Smith, Analysing Ecological Data, Springer Science, 2007 (non-obligatoire)
Teaching materials
- Dias cours magistraux, syllabus TP, bases de données, codes informatiques. Site web auto-apprentissage.
Faculty or entity
BIOL
