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.
Discover how linear programming and Python’s PuLP library can efficiently solve staff scheduling challenges, minimizing costs while meeting operational demands.
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.
A deep dive into the relationship between OLS and Theil-Sen estimators, revealing their connection through weighted averages and robust median-based slopes.
Explore exchange rate models like Purchasing Power Parity (PPP) and Uncovered Interest Parity (UIP), key frameworks in global economics.
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.
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.
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.
Explore key metrics for evaluating classification and regression models.
Understand how decision tree algorithms split data and how pruning improves generalization.