Literature

• Glen Cowan: Statistical Data Analysis. The course is heavily based on this book.

Lecture notes

• Here is a script for the videos below. It is a slightly improved version of the lecture notes; some topics are a bit more developed in the script than in the lecture notes.
• The PDF with lecture notes contains just bullet points and short explanations.

Videos

Introduction to statistics

• Video 1: Definition of probability
• Video 2: Probability density functions 1
• Video 3: Monte Carlo method
• Video 4: Probability density functions 2
• Video 5: Probability density functions 3

Parameter estimation

• Video 6: Estimators
• Video 7: Interval estimation
• Video 8: Maximum Likelihood Estimators
• Video 9: Variance of Maximum Likelihood Estimators
• Video 10: Special types of Maximum Likelihood fits
• Video 11: The Least Squares Method
• Video 12: Template fits

Hypothesis testing

• Video 13: Hypothesis testing – Introduction 1
• Video 14: Hypothesis testing – Introduction 2
• Video 15: Hypothesis tests based on binned data
• Video 16: Discovery cookbook
• Video 17: Limit setting cookbook
• Video 18: Look Elsewhere Effect

Multivariate analysis

• Video 19: Introduction to Multivariate Analyses
• Video 20: Simple MVA methods
• Video 21: Neural Networks
• Video 22: Boosted Decision Tree

Unfolding

• Video 23: Unfolding: definition of the problem
• Video 24: Unfolding with the Maximum Likelihood Method
• Video 25: Regularized unfolding
• Video 26: D’Agostini Bayesian Unfolding
• Video 27: Fully Bayesian Unfolding
• Video 28: Uncertainties on the unfolded spectrum

Homework assignments

Homework assignments are available on the following GitLab page:

https://gitlab.cern.ch/scheiric/StatMethodsCode/-/blob/master/README.md#homework

Examples

Code examples are available here:

https://gitlab.cern.ch/scheiric/StatMethodsCode/-/blob/master/README.md#examples