App to carry out simple experiments of the use of LSTM networks in time series forecasting. It allows users to solves univariate and multivariate regression problems by testing different set of ...

Objective: This study aims to investigate the association between skeletal muscle mass (SMM) and left ventricular mass (LVM), providing a basis for health management and cardiac health interventions ...

Abstract: This paper describes regression models to forecast solar irradiance for a short term (or period). The regression models enable the prediction of solar irradiance in minute values over a ...

Several examples of multivariate techniques implemented in R, Python, and SAS. Multivariate concrete dataset retrieved from https://archive.ics.uci.edu/ml/datasets ...

Have you ever found yourself staring at a spreadsheet, trying to make sense of all those numbers? Many face the challenge of transforming raw data into actionable insights, especially when it comes to ...

When you perform regression analysis in Microsoft Excel, you are engaging in a statistical process that helps you understand the relationship between variables. This technique is particularly useful ...

This paper critically examines ‘kitchen sink regression’, a practice characterised by the manual or automated selection of variables for a multivariable regression model based on p values or ...

Introduction: The search for biomarkers to predict radiosensitivity is important not only to individualize radiotherapy of cancer patients but also to forecast radiation exposure risks. The aim of ...

Given the ubiquity of amide coupling reactions, understanding the factors which influence the success of the reaction and having means to predict the reaction rate would streamline synthetic efforts.