A Guide to Bayesian A/B Testing for Conversion Rates
Explore Bayesian A/B testing as a powerful framework for analyzing conversion rates, providing more nuanced insights than traditional frequentist approaches.
Recent posts
Levene’s Test vs. Bartlett’s Test: Checking for Homogeneity of Variances
Levene’s Test and Bartlett’s Test are key tools for checking homogeneity of variances in data. Learn when to use each test, based on normality assumptions, and how they relate to tests like ANOVA.
Optimizing Staff Scheduling with Linear Programming
Discover how linear programming and Python’s PuLP library can efficiently solve staff scheduling challenges, minimizing costs while meeting operational demands.
Connection Between OLS and Theil-Sen Estimators
A deep dive into the relationship between OLS and Theil-Sen estimators, revealing their connection through weighted averages and robust median-based slopes.
Finite Difference Methods and the Black-Scholes-Merton Equation: A Numerical Approach to Option Pricing
Explore how Finite Difference Methods and the Black-Scholes-Merton differential equation are used to solve option pricing problems numerically, with a focus on explicit and implicit schemes.
Supply Chain Optimization and Industrial Network Analysis Using Data Science
Discover how data science enhances supply chain optimization and industrial network analysis, leveraging techniques like predictive analytics, machine learning, and graph theory to optimize operations.
Exploring Classic Linear Programming (LP) Problems and Scalable Solutions: A Deep Dive into PDLP
Linear Programming is the foundation of optimization in operations research. We explore its traditional methods, challenges in scaling large instances, and introduce PDLP, a scalable solver using first-order methods, designed for modern computational infrastructures.
Crime Analysis Using K-Means Clustering: Enhancing Security through Data Mining
This article explores the use of K-means clustering in crime analysis, including practical implementation, case studies, and future directions.
Building Linear Regression from Scratch: A Detailed Algorithmic Approach
A step-by-step guide to implementing Linear Regression from scratch using the Normal Equation method, complete with Python code and evaluation techniques.
A Guide to Regression Tasks: Choosing the Right Approach
Regression tasks are at the heart of machine learning. This guide explores methods like Linear Regression, Principal Component Regression, Gaussian Process Regression, and Support Vector Regression, with insights on when to use each.