Statistics for Experimentation

After reminders on 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 model with fixed effects). The emphasis is on the conditions underlying the application of statistical methods, the validation of the statistical models used and the interpretations of the software outputs. The R software, under the Commander interface, is used for statistical processing.

• Be able to propose an experimental design adapted to the questions posed and the framework concerned.
• To be able to carry out a statistical study adapted to the context in question, and to draw well-founded conclusions from the questions posed.

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.