Statistics for the Experiment

After reviewing statistical inference (estimation, tests for one or two populations), the course introduces the classical experimental designs used in agronomy for one or two factors and focuses on the standard statistical approaches associated with them (ANOVA, fixed-effects Gaussian linear model). Emphasis is placed on the conditions underlying the application of statistical methods, the validation of the statistical models used, and the interpretation of software output. The R software, using the Rcommander interface, is used for statistical analysis.

• Be able to propose a design of experiment that is appropriate for the research questions and the relevant research context.
• Be able to conduct a statistical analysis appropriate to the given context and draw well-reasoned conclusions regarding the questions posed.

Hourly volumes*:

            CM: 27

            TD: 27

• Descriptive Statistics

Recommended prerequisites*:

• Knowledge of probabilistic tools: random variable; discrete and continuous probability distributions; expected value and variance of a random variable.