Skip to content
master
Switch branches/tags
Go to file
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 

README.md

Further Hypothesis Testing

Building on the material covered by Introduction to Sampling and Hypothesis Testing, this workshop will introduce a range of commonly used hypothesis tests.

The following topics will be covered:

  • The t-test
  • Comparing variances
  • ANOVA
  • Chi-squared test
  • Testing for normality
  • Correcting for multiple tests
  • Goodness of fit

Setup

We will be working with jupyter notebooks. The easiest way to access jupyter is via the Anaconda platform. Please install Anaconda from https://www.anaconda.com in advance of the workshop.

NB no knowledge of programming is required for this workshop.

Getting Started

Download this repository to your computer as a ZIP file and unpack it.

Open JupyterLab (within Anaconda) and navigate to the unpacked directory to load the notebooks.

Alternatively, you can run the notebooks online using Binder: Binder

Evaluation

Your feedback is very important to the Graduate School as we are continually trying to improve the training we offer.

At the end of the course, please help us by completing the evaluation form at http://bit.ly/rcds2021


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

About

Imperial College London / Graduate School / Data Science / Further Hypothesis Testing

Resources

Releases

No releases published

Packages

No packages published