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Implementing QSPR modeling via multiple linear regression analysis to operations research: a study toward nanotubes
Chemical graph theory significantly predicts multifarious physio-chemical properties of complex and multidimensional compounds when investigated...
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Linear and Non-linear Regression for Gaussian Data
One of the most common tasks in the analysis of scientific data is to establish a relationship between two quantities. Many experiments feature the...
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Theoretical simulation of the structure–activity relationship of polyimide dielectric constant and analysis of its linear regression model
In this paper, two calculation methods, quantum mechanics and molecular dynamics, are used to simulate the polyimides with 12 different molecular...
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Exploration of Wi-Fi-Based Indoor Positioning System Using Linear Regression and K-Nearest Neighbour
In recent years, the use of Wi-Fi signals and machine learning techniques for indoor positioning has shown promising results. However, challenges...
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Estimating PM2.5 Concentration Using Multiple Linear Regression (MLR) and the Random Forest (RF) Approach in Jakarta, Indonesia
Jakarta, a vast urban sprawl, undergoes rapid economic development accompanied by a vast population, land-use change expansion, and increased energy...
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Multi-variable Regression
In many situations, a variable of interest depends on several other variables. Such multi-variable data are common across the sciences and in many...
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Spectral reflectance reconstruction based on multi-target regression with two-directional stacking
A multi-target regression with two-directional stacking is proposed and applied to spectral reflectance reconstruction. Most of the previous studies...
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Spectrophotometric Determination of the Overall Stability Constants of Complexes of Calcium(Ii) Ions with Glycine, L-Methionine, and L-Tryptophan Using Multiple Linear Regression
An accurate method was proposed for determining the overall stability constants of calcium(II) ions with glycine, L-methionine, and L-tryptophan in...
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Application of Multiple Linear Regression and Geographically Weighted Regression Model for Prediction of PM2.5
The present study deals with the assessment of the spatial distribution of PM 2.5 over a decade in Jharkhand state of eastern India, which is a...
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Ionospheric TEC forecasting using Gaussian Process Regression (GPR) and Multiple Linear Regression (MLR) in Turkey
This study aims to predict daily ionospheric Total Electron Content (TEC) using Gaussian Process Regression (GPR) model and Multiple Linear...
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The Linear Correlation Coefficient
The linear correlation coefficient r is a simple and convenient measure of the degree of linear correlation between two random variables, and it is...
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Harnessing data using symbolic regression methods for discovering novel paradigms in physics
In recent years, machine-learning methods have profoundly impacted research in the interdisciplinary fields of physics. However, most...
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Multivariate Regression Analysis and Error Estimation in Formation Satellite
AbstractIn this work, we aim to develop a non-linear multivariate regression model, which predicts the initial condition for periodic orbits of...
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Automatically discovering ordinary differential equations from data with sparse regression
Discovering nonlinear differential equations that describe system dynamics from empirical data is a fundamental challenge in contemporary science....
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Modern Approaches to Statistical Estimation of Measurements in the Location Model and Regression
Metrology as the science about measurement is highly intertwined with statistical point estimation. Evaluating and controling uncertainty of...
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Parton labeling without matching: unveiling emergent labelling capabilities in regression models
Parton labeling methods are widely used when reconstructing collider events with top quarks or other massive particles. State-of-the-art techniques...
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The Linear Template Fit
The estimation of parameters from data is a common problem in many areas of the physical sciences, and frequently used algorithms rely on sets of...
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A Neural Regression Model for Predicting Thermal Conductivity of CNT Nanofluids with Multiple Base Fluids
High thermal conductivity of carbon nanotube nanofluids ( k nf ) has received great attention. However, the current researches are limited by...
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Correlation Method to Compensate for the Spectra of Interfering Substances in Analysis of the Isomeric Composition of Propyl Alcohol
The infrared absorption spectra of the saturated vapor of isopropyl alcohol isomers and mixtures of these isomers were investigated. A method was...
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Support vector regression-based study of interference in absorption spectral lines of mixed gases
When measuring the concentration of multi-component gas mixtures based on supercontinuum laser absorption spectroscopy (SCLAS), there are...