Statistics course for PhD students

Introduction

Welcome to the Statistics for Physical Scientists short course! It's designed to give researchers, particularly in the physical sciences, some practical background and guidance in applying common statistical tools. The course covers: The full introduction and content summary can be found here The course is structured in 6 classes, as described below, which are split into content presentation, worked examples and practical activities using the datasets provided. Each class comes with an accompanying python Jupyter notebook, which provides summary notes and code for all the worked examples.

Useful books

The following is an (incomplete!) list of books which contain a great deal of practical wisdom in using statistics:

Class material

Datasets

Here are the datasets that are used in the worked examples and activities:

Class 1: Probability and statistics

Here are the Class 1 content slides as pdf and powerpoint Here is the accompanying python Jupyter notebook for Class 1.

Class 2: Correlation Testing

Here are the Class 2 content slides as pdf and powerpoint Here is the accompanying python Jupyter notebook for Class 2.

Class 3: Model Fitting

Here are the Class 3 content slides as pdf and powerpoint Here is the accompanying python Jupyter notebook for Class 3.

Class 4: Regression

Here are the Class 4 content slides as pdf and powerpoint Here is the accompanying python Jupyter notebook for Class 4.

Class 5: Error Estimates

Here are the Class 5 content slides as pdf and powerpoint Here is the accompanying python Jupyter notebook for Class 5.

Class 6: Bayesian Methods

Here are the Class 6 content slides as pdf and powerpoint Here is the accompanying python Jupyter notebook for Class 6.

Link for old stats course