Almost everyone who does research needs statistics: when you have acquired research scientific data, you typically want to use them to show that your data provide significant support for some hypothesis, or if they allow to reject that hypothesis.
The Python scripts in this repository accompany the book "Introduction to Statistics with Python", and provide working examples of a large number of statistical tests. So if any of those tests are required for the analysis of your own data, you should be able to take the corresponding script, "plug in" your own data - and you are done! The text in the book should provide enough information so that you know what you are doing (in case you don't know that already), and where to go next if you are looking for a more elaborate statistical analysis of your data.
The Python sample scripts can be found in the folder Code_Quantlets, and provide examples of:
- How to visualize statistical data sets.
- How to work with statistical distributions.
- Tests of mean values of one, two, or more groups of data.
- Tests on categorical data.
- Analysis of survival times and reliability data.
- Simple examples of linear regression models.
- Example of statistical bootstrapping.
- Tests on discrete data, e.g. logistic regression.
- Application of Bayesian statistics.
Additional Python scripts can be found in the following folders, sorted by Chapter/Subchapter:
- Figures ... Python code for the generation of figures.
- Listings ... code listings that are used in the book.
- Solutions ... Solutions to exercises presented in the book.