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.
Granger Causality Test: Assessing Temporal Causal Relationships in Time-Series Data
Explore the Granger causality test, a vital tool for determining causal relationships in time-series data across various domains, including economics, climate science, and finance.
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.
Exchange Rate Models: Understanding PPP and UIP
Explore exchange rate models like Purchasing Power Parity (PPP) and Uncovered Interest Parity (UIP), key frameworks in global economics.
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.