Excel portfolio project combining Fama–French 3-factor regressions with Markowitz optimization. Calculates expected returns, covariances, and solves for Minimum Variance & Tangency Portfolios.

ABSTRACT: This study presents the Dynamic Multi-Objective Uncapacitated Facility Location Problem (DMUFLP) model, a novel and forward-thinking approach designed to enhance facility location decisions ...

Linear regression remains a cornerstone of statistical analysis, offering a framework for modelling relationships between a dependent variable and one or more independent predictors. Over the past ...

Fuzzy regression models extend traditional statistical regression by integrating fuzzy set theory to better handle imprecision and uncertainty inherent in many real-world data sets. These models ...

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression using JavaScript. Linear regression is the simplest machine learning technique to predict a single numeric value, ...

Implement Linear Regression in Python from Scratch ! In this video, we will implement linear regression in python from scratch. We will not use any build in models, but we will understand the code ...

In this episode of eSpeaks, Jennifer Margles, Director of Product Management at BMC Software, discusses the transition from traditional job scheduling to the era of the autonomous enterprise. eSpeaks’ ...

In this episode of eSpeaks, Jennifer Margles, Director of Product Management at BMC Software, discusses the transition from traditional job scheduling to the era of the autonomous enterprise. eSpeaks’ ...

To address the limitations of commonly used cross-validation methods, the linear regression method (LR) was proposed to estimate population accuracy of predictions based on the implicit assumption ...

Abstract: Linear regression is the most frequently used regression analysis due to its simplicity in predicting and forecasting. However, because of its parametric approach, it is necessary to test ...