1 – Understanding the theory / foundations of the discipline
The primary goals of this course are to provide students with a comprehensive understanding of the probability theory and statistics, by getting overview of foundations and theories.
2 – Understanding the quantitative ontologial nature of our world
Students will be able to know the ontological mathematical nature of systems, especially biological systems, and will be able to handle every possible mathematical tools and theories to extract knowledge from them, from the real world.
3 – Being autonomous in the journey of experimental design and statistical modelization
By the end of this course, students should be equipped with the knowledge and skills necessary to analyze and interpret complex data, make informed decisions, and apply statistical methods in various real-world scenarios.
We will understand that the real world is complex and that we can use different tools to handle this complexity. The ultimate purpose will be ​​to create the ability to choose the best tools depending on the nature of the data (experimental design, linearity, parametric, or not)
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This cours is the continuity of the course “Probability and Statistics”, but based on real-world application and focusing on statistical models
STEP 5 _ LINEAR MODELS EXAMPLES
5.1 – SIMPLE LINEAR REGRESSION
5.2 – MULTIPLE LINEAR REGRESSION
5.3 – OTHER REGRESSION MODELS
STEP 6 _ OTHER CLASSIC MODEL EXAMPLES
6.1 – UNIVARIATE TESTING
6.2 – MULTIVARIATE TESTING
6.3 – NON PARAMETRIC STATISTICS
STEP 7 _ NON-LINEAR MODELS EXAMPLES
7.1 – PROBABILISTIC GRAPHICAL MODELS
7.2 – PERCOLATION THEORY
7.3 – SPATIAL STATISTICS
7.4 – EXTREM VALUE THEORY
