A Predictive Model of the Photosynthetic Rate of Chili Peppers Using Support Vector Regression and Environmental Multi-Factor Analysis by Bin Li, Bo Qiao, Qianyu Zhao, Dan Yang, Rongcheng Zhu, Zhexuan Wang, Yujie Yang

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Spatiotemporal Variation in Carbon and Water Use Efficiency and Their Influencing Variables Based on Remote Sensing Data in the Nanling Mountains Region by Sha Lei, Ping Zhou, Jiaying Lin, Zhaowei Tan, Junxiang Huang, Ping Yan, Hui Chen

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Study of linear and nonlinear isotherm and kinetic parameters of hexavalent chromium adsorption onto reduced graphene oxide coated iron oxide by Selim Gürsoy, Nagehan Kübra Zeytinci, Buse Tuğba Zaman, Sezgin Bakırdere, Elif Öztürk Er

“…The batch adsorption process was optimized by conducting response surface methods for assessing primary variables affecting the adsorption process. Adsorption equilibrium mechanism was analyzed through the application of Langmuir and Freundlich isotherm models, utilizing both linear and nonlinear regression approaches. …”
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Remote sensing and field-based estimation of aboveground biomass of plantation forests: Kofale, South East Ethiopia by Abdi Gudisa, Habitamu Taddese, Jatani Garbole

“…Multi-linear regression with a stepwise selection method was employed to refine the predictor variables, adjusting forward selection (prem = 0.05) and backward elimination (penter = 0.1). …”
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Informativeness (information-bearing) of hydrometeorological and astrogeophysical factors in the problem of describing interannual fluctuations of the Greenland Sea ice coverage by N. A. Viazigina, L. A. Timokhov, E. S. Egorova, A. V. Yulin

“…When constructing the multi-regression equations, we investigated the informativeness of various hydrometeorological and astrogeophysical factors in the models of the ice coverage variability for each season. …”
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The Effects of Speed and Flow Characteristics on Crash Rates for Wasit Multi-lane Highways in Iraq by Amjad H. Albayati, Zahraa A. Ramadan

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Regression model of fear of childbirth in pregnant women by Farzaneh Rashidi, Nazanin Hesari, Sahar Shariatnia, Abdollah Razi, SeyyedMohammad MohammadiAubi, Fatemeh Gorji, Faezeh Ghanbari

“…Data analysis was performed using SPSS version 24, utilizing descriptive statistics, t-tests, and multiple linear regression. Results The results of multiple linear regression indicate that the variables of age (B = 0.257), women’s education(B = 2.54), spouse’s education (B = 3.87), and preferred delivery(B = 7.097) can significantly predict the variance in fear of childbirth. …”
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Exploring multi-scale spatial relationship between built environment and public bicycle ridership: A case study in Nanjing by Cheng Lyu, Xinhua Wu, Yang Liu, Zhiyuan Liu, Xun Yang

“…The proposed method outperforms linear regression and standard geographically weighted regression (GWR) in terms of explanatory power. …”
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Optimization of Cold Spray Nozzles Based on the Response Surface Methodology by Kun T., Wenjie H., Yurong W.

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Raman-based PAT for multi-attribute monitoring during VLP recovery by dual-stage CFF: attribute-specific spectral preprocessing for model transfer by Annabelle Dietrich, Luca Heim, Jürgen Hubbuch 

“…As a result of this increased exposure time, the predominant sulfate band exhibited oversaturation effects, which otherwise could have been used for AMS quantification via linear regression. Nevertheless, AMS prediction using purpose-driven preprocessing operations and PLS models was achieved with normalization and a data-driven variable selection technique, next to baseline correction and signal smoothing. …”
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Physical function evaluation in volleyball training based on intelligent GRNN by Kaiyuan Dong, Borhannudin bin Abdullah, Hazizi bin Abu Saad, Chenxi Lu

“…Firstly, based on the framework of the generalized regression neural network, a variable-structure generalized regression neural network (VSGRNN) is proposed and developed. …”
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A Weighted Bayesian Kernel Machine Regression Approach for Predicting the Growth of Indoor-Cultured Abalone by Seung-Won Seo, Gyumin Choi, Ho-Jin Jung, Mi-Jin Choi, Young-Dae Oh, Hyun-Seok Jang, Han-Kyu Lim, Seongil Jo

“…The proposed method employs a weighted Bayesian kernel machine regression model, integrating Gaussian processes with a spike-and-slab prior to identify influential variables. …”
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Biogeographic distribution and environmental drivers of tetracycline resistance genes in urban green spaces by Xunqiang Mo, Lingyue Lv, Jianzhong Xu, Zirui Meng, Xin Wen, Yu Liu, Runqiu Feng, Kai He, Jie Liu, Mengxuan He

“…In this study, we compared the performance of multiple linear regression (MLR), geographically weighted regression (GWR), and multi-scale geographically weighted regression (MGWR) models in predicting the spatial distribution of tet ARGs across urban green spaces in Tianjin, China, using eight environmental and anthropogenic variables. …”
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Interpretable graph Kolmogorov–Arnold networks for multi-cancer classification and biomarker identification using multi-omics data by Fadi Alharbi, Nishant Budhiraja, Aleksandar Vakanski, Boyu Zhang, Murtada K. Elbashir, Harshith Guduru, Mohanad Mohammed

“…The proposed approach combines differential gene expression with DESeq2, Linear Models for Microarray (LIMMA), and Least Absolute Shrinkage and Selection Operator (LASSO) regression to reduce multi-omics data dimensionality while preserving relevant biological features. …”
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Correction of TRMM 3B43 Monthly Precipitation Data Using Quantile Regression Model in the Urmia Lake Basin by Sima Kazempour Choursi, Mahdi Erfanian, Hirad Abghari, Mirhassan Miryaghoobzadeh, khadijeh Javan

“…In all months, the Kling-Gupta efficiency (KGE) values showed a rise when the quantile regression method was used instead of the original TRMM data and the linear regression method. …”
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Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model. by Rui Meng, Kristofer E Bouchard

“…Here, we developed a new latent process Bayesian regression framework, the orthogonal stochastic linear mixing model (OSLMM) which introduces an orthogonality constraint amongst time-varying mixture coefficients, and provide Markov chain Monte Carlo inference procedures. …”
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Advancement of Artificial Intelligence in Cost Estimation for Project Management Success: A Systematic Review of Machine Learning, Deep Learning, Regression, and Hybrid Models by Md. Mahfuzul Islam Shamim, Abu Bakar bin Abdul Hamid, Tadiwa Elisha Nyamasvisva, Najmus Saqib Bin Rafi

“…Machine learning models achieve an average accuracy of 75–80%, providing strong performance, particularly in industries like road construction and healthcare. Regression models typically deliver 70–80% accuracy, being more suitable for simpler cost estimations where the relationships between variables are linear. …”
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Shoulder Pain in Competitive Swimmers: A Multi-Site Survey Study by Brian D. Stirling, Jonathan C. Sum, Lisa Baek, Lori A. Michener, Adam J. Barrack, Angela R. Tate

“…Independent t-tests were used to compare pain, disability, dissatisfaction, the influence of age, sex, participation in second sport, geographic region, and history of shoulder pain. Linear regression analyses were performed to determine the interaction of these variables with reported pain and disability…”
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Socioeconomic disparities in mortality from indoor air pollution: A multi-country study. by Muayad Albadrani

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An Iterative Shifting Disaggregation Algorithm for Multi-Source, Irregularly Sampled, and Overlapped Time Series by Colin O. Quinn, Ronald H. Brown, George F. Corliss, Richard J. Povinelli

“…ISD operates in an iterative, two-phase process: a prediction phase that uses multiple linear regression to generate high-frequency series from low-frequency data and correlated variables, followed by an update phase that redistributes low-frequency observations across high-frequency periods. …”
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