Statistics for Experimentation

After a review of statistical inference (estimation, tests on one or two populations), the course introduces the classical experimental designs used in agronomy for one or two factors and focuses on the usual statistical approaches related to them (ANOVA, linear Gaussian fixed effects model). Emphasis is placed on the conditions underlying the application of statistical methods, the validation of the statistical models used and the interpretation of the software outputs. The R software, under the Rcommander interface, is used for the statistical treatments.

• Be able to propose an experimental design adapted to the questions asked and the framework concerned.
• Be able to conduct the statistical study adapted to the framework considered and to draw reasoned conclusions regarding the questions asked.

Hourly volumes* :

            CM : 27

            TD : 27

• Descriptive statistics

Recommended prerequisites*:

• Knowledge of probabilistic tools: random variable; discrete and continuous probability laws; expectation and variance of a random variable.