A package for statistically rigorous scientific discovery using machine learning. Implements prediction-powered inference.

A comprehensive exploration of Statistics and Probability Theory concepts, with practical implementations in Python

Multiple hypothesis testing in Python

Credit Risk Modelling | Calculation of PD, LGD, EDA and EL with Machine Learning in Python

Statistical functions based on bootstrapping for computing confidence intervals and p-values comparing machine learning models and human readers

Minimal A/B Testing Library in PHP

Analysis platform for large-scale dose-dependent data

E-Commerce Website A/B testing: Recommend which of two landing pages to keep based on A/B testing

pMoSS (p-value Model using the Sample Size) is a Python code to model the p-value as an n-dependent function using Monte Carlo cross-validation. Exploits the dependence on the sample size to characterize the differences among groups of large datasets

Bootstrap p-values, including convenience functions for regression models.

Implementation of backward elimination algorithm used for dimensionality reduction for improving the performance of risk calculation in life insurance industry.

Lean Six Sigma with Python — Kruskal Wallis Test

Assignment-04-Simple-Linear-Regression-2. Q2) Salary_hike -> Build a prediction model for Salary_hike Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization. Correlation Analysis. Model Building. Model Testing. Model Predictions.

Adjust p-values for multiple comparisons

Generalized linear mixed model elastic net

collection of utility functions for correlation analysis

Strategies for analyzing the distribution of datasets, switching the data towards a normal distribution testing different manual transformations and Box-Cox transformation.

Shiny Web Application for Making Your p-value Sound Significant

Udacity Data Analyst Nanodegree - Project III

This repository provides MATLAB implementations of plfit and plpva functions for fitting power-law distributions to empirical data using maximum likelihood estimation (MLE) and statistical goodness-of-fit tests. These tools accurately model complex systems with significant tail behaviors, common in fields like physics, biology, and economics.