Experimental design and data analysis with R


Lecturer(s) :




pas donné en 2019-20


Linking together the elements of a research project. Basic principles of designing experiments and observational studies. Statistical model of Multiple regressions and Analysis of variance, as special cases of the general linear model, Data analysis with the statistical software R.


  1. Introduction (goal of the course, prerequisite, what is R)
  2. Two examples: The Jura Gradient Experiment and the Doubs River environmental study
  3. An introduction to basic coding in R
  4. Designing experiments and observational studies (Basic Principles, Power Analysis and Number of Replications, Some Types of Experimental Designs, Some Types of Sampling Designs for Observational Studies)
  5. Statistical models (General linear models, models with quantitative explanatory variables (multiple regression), models with categorical explanatory variables (ANOVA Models), models with both quantitiative and categorical explanatory variables (ANCOVA Models), theoretical foundation of the analysis of general linear models)
  6. Principles of data analysis (Hypotheses to be tested, analysis of multiple regression, analysis of variance (ANOVA), analysis of covariance (ANCOVA), including Model Checking)
  7. Analysing specific experiments: completely randomized experiment with one factor, completely randomized experiment with subsampling (Icludung mix-effect model), completely randomized experiment with repeated measurements, complete randomized blocks designs, split-plot experiments, Ancova models)
  8. Analysing specific observational studies (simple random sampling, systematic sampling, stratified sampling), including Principal Component Analysis and Cluster Analysis)
  9. Special Issues (model assumptions not fulfilled, count data, unbalanced designs, pseudo-repetitions, effect size, contrasts)


Experimental design, sampling design, linear models, multiple regression, analysis of variance, data analysis, statistical software R.

Learning Prerequisites

Required courses

Probability and Statistics, Prof. Victor Panaretos, Bachelor semester 2" or another course with a similar content (statistical distributions, expected value, error types one and two, parameter estimation, testing hypotheses, statistical significance, simple linear regression, one way analysis of variance).


Recommended courses

Multivariate Statistics with R, ENV 521, Prof. Alexandre Buttler

Important concepts to start the course

The basic elements of R (what is R ?, installing R, packages and functions, using R as a calculator, importing data with R, reading data, simple descriptive statistics with R, simple tests of hypotheses with R, simples graphs with R)

The basic elements of statistics (sample and population, parameters of position (mean, median, mode), parameters of dispersion (standard deviation, variance, coefficient of variation, range), distribution of a variable (Normal, t, F, chi-square), estimation (standard error, confidence interval), null hypothesis vs alternative hypothesis, error type 1 and error type 2, statistical tests, simple linear regression, one way ANOVA, histogram, scatterplot)

 Participants without these prerequistes are invited to update their knowledge before the beginning of the course.

Learning Outcomes

Transversal skills

Teaching methods



Expected student activities

attendance at the lectures

completing exercises

reading written material (given documents, documents on the web)

Assessment methods

Exercises during the semester (30% of the final grade)

Written exam during the examination period (70% of the final grade)



Office hours Yes
Assistants Yes



- Borcard, Daniel; Gillet, François; Legendre, Pierre. 2011. Numerical Ecology with R. Springer.

- Cochran William G. 1977. Sampling Techniques. Third Edition. Wiley. 474 pp.

Note: This edition is freely available on internet. Cochran?s book is one of the fundamental work on sampling.

- Crawley Michael J. 2015. The R Book. Second Edition. Wiley. 1051 pp.

Note: The first edition of this book is freely available on internet.

- Crawley Michael J. Statistics. An introduction using R. Second Edition. 359 pp. Is a it-ebook (see Free available on Internet

- Davison A. C. & Kuonen, D. (2013). Probabilités et Statistique pour Sciences de l'Environnement.Polycopié disponible à la "Librairie Polytechnique" de l'EPFL. (Edition 2013 modifiée par V.M. Panaretos).

- Lawson John. 2015. Design and Analysis of Experiments with R. CRC Press. 506 pp.

- Montgomery Douglas C. 2013. Design and Analysis of Experiments. Eights Edition. Wiley. 730 pp.

Note: Montgomery is one of the leading experts in Experimental Design. The fifth edition of is book is freely available on Internet.

 - Quinn Gerry P., Keough Michael J. 2002. Experimental Design and Data Analysis for Biologists. Cambridge. 537 pp., is freely available on Internet

 - Sutherland William J. 2006. Ecological Census Techniques. A handbook. Second Edition. Cambridge


Ressources en bibliothèque

In the programs

Reference week

      Exercise, TP
      Project, other


  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German