pure-Python HistFactory implementation with tensors and autodiff

statistics.js is a lightweight library to provide all basic and many advanced utilities commonly used in statistical data analysis. It is free to use, open source and open for contributions.

Introduction to statistics featuring Python. This series of lecture notes aim to walk you through all basic concepts of statistics, such as descriptive statistics, parameter estimations, hypothesis testing, ANOVA and etc. All codes are straightforward to understand.

Valid and adaptive prediction intervals for probabilistic time series forecasting

Hypothesis and statistical testing in Python

Personal notes on statistics with a focus on applications to experimental high energy physics

A MATLAB package for multivariate permutation testing and effect size measurement

Python package for Bayesian & Frequentist A/B Testing

A curated collection of free resources to help the aspiring computational biologist learn about the R programming language.

A 30+ node flowchart for selecting the right statistical test for evaluating experimental data.

Frequentist Sequential A/B Testing using Random Walks

I will include two ways of t tests that compare conversion rate and click through rate of two groups

Simple coin tossing simulation to show the issues with peeking at data during frequentist A/B tests

Refreshing experiment analysis in python

EP4130 - Spring 2023: This course is a "starters kit" of useful statistical tools needed for analysis of data. It will discuss usage of statistics in Astronomy and Particle Physics and what tools are need to tackle problems in the above fields. However, the same statistical techniques are used in all branches of Physics and Engineering

This incomplete repository is used to facilitate the consultation of individual files in this project. Only files smaller than 100 MB are available here. The complete project is available at https://doi.org/10.17605/OSF.IO/GT5UF.

A series of statistical models applied to different case studies

Capturing notes from a basic statistics class into jupyter notebooks and embellishing with MathJax formulas, python code and visualizations

A small Shiny apps and R Presentation on a small algorithm I used to write to estimate a small problem in my senior year.

Data 102 Final Project