Course

MSE-213: Probability and statistics for materials science

Summary

The course establishes the concepts of statistical analysis, probability theory, and data analysis. This includes standard statistical tests, correlation analysis and experimental design. It introduces computational statistical methods to analyse large data sets.

Lectures

Instructor

Gregor Jotzu

Gregor Jotzu is heading the Dynamic Quantum Materials Laboratory (DQML) at EPFL. His research concerns the control of quantum materials using intense low-frequency laser pulses and the dynamics of order formation in strongly correlated quantum systems.After reading physics at the University of Oxford, he became the 2009/2010 Michael von Clemm Fellow at Harvard University. He received his doctorate from ETH Zurich, where he studied quantum magnetism and driving-induced order using ultracold atoms in optical

Official source

https://edu.epfl.ch/coursebook/en/probability-and-statistics-for-materials-science-MSE-213

Moodle Page

http://go.epfl.ch/MSE-213

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Summary

Lectures

Instructor

Gregor Jotzu

Official source

https://edu.epfl.ch/coursebook/en/probability-and-statistics-for-materials-science-MSE-213

Moodle Page

http://go.epfl.ch/MSE-213

About this result

Lectures in this course (35)

ANOVA: Partitioning Total SS

Covers ANOVA method, focusing on partitioning total sum of squares into treatment and error components, mean square calculations, Fisher statistic, and F-distribution.

Multi-factor ANOVA

Explores Multi-factor ANOVA, analyzing variance explained by multiple factors and their interactions, providing practical hints on decomposing total sum of squares.

Model with Interactions

Explains models with interactions, emphasizing the significance of factors and F-values.

Linear Regression: Basics and Principle of Least Squares

Covers linear regression basics, focusing on minimizing error using the principle of least squares and includes an ANOVA table and practical example in R.

Linear Regression: Fundamentals and Goodness of Fit

Explores linear regression fundamentals, non-linear regression issues, and R-squared goodness of fit, with examples like Anscombe's quartet and the Datasaurus dataset.

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