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

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

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

Analysis platform for large-scale dose-dependent data

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

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

collection of utility functions for correlation analysis

Lean Six Sigma with Python — Kruskal Wallis Test

Shiny Web Application for Making Your p-value Sound Significant

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.

Understand the results of an A/B test run by the website and provide statistical and practical interpretation on the test results

Assignment-05-Multiple-Linear-Regression-2. Prepare a prediction model for profit of 50_startups data. Do transformations for getting better predictions of profit and make a table containing R^2 value for each prepared model. R&D Spend -- Research and devolop spend in the past few years Administration -- spend on administration in the past few y…

Udacity Data Analyst Nanodegree - Project III

Analysis of mock A/B Test Results by an e-commerce company. Application of probability, hypothesis testing, sampling distribution, two-sample z-test, and logistic regression to determining whether the company should implement the new web page it developed to increase users' conversion rate