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.

Lean Six Sigma with Python — Kruskal Wallis Test

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

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

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

collection of utility functions for correlation analysis

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.