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# Theory and Practice for Advanced Statistics

The main objective of this course is to review the linear model and introduce some of its extensions, namely mixed linear models, generalized linear models, nonlinear regression. The last three lectures will introduce Bayesian principles, from a parametric and nonparametric point of view. Focus will be on explaining the theory in a synthetic manner and apprehend how the proposed models can be estimated using R. A side goal is to get familiar with the statistical vocabulary and be able to understand what black box/packages are actually doing to help acquiring a critical eye on the results of biological or agricultural data analysis.

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3912171
Theory and Practice for Advanced Statistics

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●Lecture 1: From simple linear regression to Ancova analysis. ●Lecture 2: Linear mixed models ●Lecture 3: Generalized Linear Models, ridge/lasso regression, and introduction to Generalized Additive Models ●Lecture 4: Nonlinear regression and ●Lecture 5: Bayesian main principles ●Lecture 6: introduction to Bayesian nonparametrics ●Lecture 7: MCMC approaches.

There will be no specific textbook, and lectures will be given on the basis of resumes distributed each time. Three small reports presenting applications of the models presented in class to personal datasets will be required to consolidate the basic knowledge learned in the class.

Comprehensive evaluation will be made based on attendance, and small reports.

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By taking this course, students will gain knowledge of statistical vocabulary and advanced approaches to analyze various biological datasets. They will deepen their understanding of the underlying statistics, ideally using their own datasets. The amount and type of data in agriculture and biology will continue to increase in the future, and knowledge and understanding of these methods will become increasingly important in future agricultural and biological research.
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Lectures are conducted in an online format (fully online). Language used: English (Medium of instrucion: E、Course materials: E) Lecturer writing this syllabus: Tressou Jessica