David Gerbing from the School of Business at Portland State University introduces lessR, a tool designed to facilitate professional-quality data visualizations and data analysis without programming re ...

Uncover the hidden pitfalls of Excel regression and learn why Python is the key to unlocking clean, efficient data analysis.

Objectives To characterise the non-linear joint dose–response relationship of accelerometer-measured moderate-to-vigorous physical activity (MVPA) and cardiorespiratory fitness (CRF, estimated as ...

Abstract: The identification of changes in linear models is a fundamental problem encountered in various applications. Traditional methods often encounter difficulties when attempting to identify ...

Linear regression is the most fundamental machine learning technique to create a model that predicts a single numeric value. One of the three most common techniques to train a linear regression model ...

Abstract: In this article, a sparse signal recovery algorithm using Bayesian linear regression with Cauchy prior (BLRC) is proposed. Utilizing an approximate expectation maximization (AEM) scheme, a ...

ABSTRACT: This study employs machine learning techniques (AI), specifically multiple linear regression with nonlinear covariates, to model rental prices per square meter in Berlin, Germany. The ...

This study introduces an XGBoost-MICE (Multiple Imputation by Chained Equations) method for addressing missing data in mine ventilation parameters. Using historical ventilation system data from ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the linear support vector regression (linear SVR) technique, where the goal is to predict a single numeric ...

ABSTRACT: This paper investigates the relationship between GDP growth and imports from high income economies, low-to-medium income economies and the Arab World for 15 European Union countries having a ...