The Determinants of Commercial Land Leases in the Non-Central Districts of a Large City in China: Data Analysis from the Government–Market Perspective by Jing Cheng

“…The mathematical models used for multiple linear regression for the leased price and area of the influencing factors of commercial land leases from the perspective of the government and market are proposed. …”
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Comparative Study of Machine Learning Techniques for Predicting UCS Values Using Basic Soil Index Parameters in Pavement Construction by Mudhaffer Alqudah, Haitham Saleh, Hakan Yasarer, Ahmed Al-Ostaz, Yacoub Najjar

“…The methods employed included multi-linear regression (MLR), multi-nonlinear regression (MNLR), and several machine learning techniques: backpropagation artificial neural networks (ANNs), gradient boosting (GB), random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN). …”
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Association between adherence to behavioral intervention and capability well-being among parents of autistic children: a cross-sectional study from China by Huanyu Zhang, Shanquan Chen, Jiazhou Yu, Xuejing Niu, Xue Wang, Li Wang

“…Intervention adherence as well as the variables with p < 0.1 in the univariate analyses were included in multivariate linear regression analyses. …”
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Bipolar mood tendency and frontal activation using a multichannel near infrared spectroscopy by Toru Uehara, Yoko Ishige

“…Total MDQ were correlated significantly with frontal activation negatively in many channels; therefore, we conducted multiple linear regression to select significant frontal activations using the MDQ as a dependent variable. …”
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Dual-Task Predictors in Graduate Healthcare Students with Athletic Backgrounds by Fernando Castillo, Brittani Freund, Ryan Hulla, Janis Henricksen, Neeraj Kumar, Chad Schmeeckle, Shannon Estes, Priya Karakkattil

“…Next, principle component analyses were conducted to incorporate all measured variables into a single model for linear regression. …”
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Comparative studies of Taguchi dynamic analysis, statistical, and artificial neural networks for low-carbon steel corrosion inhibition in acidic media by Aprael S. Yaro, Anees A. Khadom, Saeed Rajab Yassen

“…In ANN analyses, five networks were suggested: linear, a generalized regression neural network (GRNN), radial basis functions (RBF), and two multi-layer perceptron (MLP) networks. …”
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Investigating the relationship between social stigma and treatment adherence in type 2 diabetes patients at healthcare centers in Northwest Iran by Maryam Sedaei, Mohammad Ail Mohamadi, Behrouz Dadkhah

“…The stepwise multiple linear regression model revealed that 15.1% of the variance in treatment adherence could be explained by age, duration of the disease, and social stigma of diabetes. …”
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The Use of Telehealth Technology in Assessing the Accuracy of Self-Reported Weight and the Impact of a Daily Immediate-Feedback Intervention among Obese Employees by Nicolaas P. Pronk, A. Lauren Crain, Jeffrey J. VanWormer, Brian C. Martinson, Jackie L. Boucher, Daniel L. Cosentino

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Plasma based markers of [11C] PiB-PET brain amyloid burden. by Steven John Kiddle, Madhav Thambisetty, Andrew Simmons, Joanna Riddoch-Contreras, Abdul Hye, Eric Westman, Ian Pike, Malcolm Ward, Caroline Johnston, Michelle Katharine Lupton, Katie Lunnon, Hilkka Soininen, Iwona Kloszewska, Magda Tsolaki, Bruno Vellas, Patrizia Mecocci, Simon Lovestone, Stephen Newhouse, Richard Dobson, Alzheimers Disease Neuroimaging Initiative

“…Thirteen of these markers of brain amyloid burden--c-peptide, fibrinogen, alpha-1-antitrypsin, pancreatic polypeptide, complement C3, vitronectin, cortisol, AXL receptor kinase, interleukin-3, interleukin-13, matrix metalloproteinase-9 total, apolipoprotein E and immunoglobulin E--were used along with co-variates in multiple linear regression, and were shown by cross-validation to explain >30% of the variance of brain amyloid burden. …”
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Remote sensing-driven machine learning models for spatiotemporal analysis of coastal phytoplankton blooms under climate change scenarios by Siqi Wang, Shuzhe Huang, Yinguo Qiu, Xiang Zhang, Chao Wang, Nengcheng Chen

“…The LightGBM model, incorporating multi-season remote sensing data and key variables, achieved the highest accuracy, with R-values of 0.95 for warning level classification and 0.6 for bloom area regression. …”
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Toward a comprehensive understanding of massive open online course adoption among college students by Qiongzhen Huang, Shan Li, Yuxia Du

“…Results from structural equation modeling and multi‐linear regression analyses revealed that students' BI was significantly and positively influenced by six factors: performance expectancy, effort expectancy, facilitating conditions (FC), social influence, SRL management (SM), and PP. …”
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Albufera Lagoon Ecological State Study Through the Temporal Analysis Tools Developed with PerúSAT-1 Satellite by Bárbara Alvado, Luis Saldarriaga, Xavier Sòria-Perpinyà, Juan Miguel Soria, Jorge Vicent, Antonio Ruíz-Verdú, Clara García-Martínez, Eduardo Vicente, Jesus Delegido

“…All possible combinations of two bands were obtained and subsequently correlated with the biophysical variables by fitting a linear regression between the field data and the band combinations. …”
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PP-QADMM: A Dual-Driven Perturbation and Quantized ADMM for Privacy Preserving and Communication-Efficient Federated Learning by Anis Elgabli

“…Extensive numerical experiments on a convex linear regression task validate the effectiveness of PP-QADMM. …”
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A machine learning approach for wind turbine power forecasting for maintenance planning by Hariom Dhungana

“…The interpretable ML includes Linear Regression (LR), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Random Forest (RF); the explainable ML consists of graphical Neural network (GNN); and the blackbox model includes Multi-layer Perceptron (MLP), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). …”
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Exploring the Potential of Lateritic Aggregates in Pervious Concrete: A Study on Mechanical Properties and Predictive Techniques by Pushparaj A. Naik, Shriram Marathe

“…Additionally, Python-based predictive models employing multi-linear regression were developed to estimate compressive strength based on independent variables such as binder quantity, coarse aggregate content, water-to-cement ratio, and curing duration. …”
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APOE4 and infectious diseases jointly contribute to brain glucose hypometabolism, a biomarker of Alzheimer's pathology: New findings from the ADNI. by Aravind Lathika Rajendrakumar, Konstantin G Arbeev, Olivia Bagley, Matt Duan, Anatoliy I Yashin, Svetlana Ukraintseva, Alzheimer’s Disease Neuroimaging Initiative

“…<h4>Methods</h4>We analyzed data on 1,509 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database using multivariate linear regression models. The outcomes were rank-normalized hypometabolic convergence index (HCI), statistical regions of interest (SROI) for AD, and mild cognitive impairment (MCI). …”
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Length of stay in the emergency department and its associated input-, throughput-, and output factors at two hospitals in Sweden by Jonas Andersson, Lisa Kurland, Lena Nordgren, Annelie K. Gusdal, Ivy Cheng

“…The explanatory factors included patient characteristics, medical data, and hospital bed occupancy data. Multi-variable linear regression analysis was used to test the associations between the factors and EDLOS. …”
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Safety Performance Functions for Low-Volume Roads by Gianluca Dell’Acqua, Francesca Russo

“…The coefficients of the CPMs are estimated using a non-linear multi-variable regression analysis utilizing the least – square method. …”
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Higher atherogenic index of plasma is associated with intradialytic hypotension: a multicenter cross-sectional study by Yanzhe Peng, Dan Shuai, Yuqi Yang, Yan Ran, Jing Yuan, Yan Zha

“…Patients were divided into four groups based on the AIP quartiles. Linear regression and multiple logistic regression models were used to analyze the relationship between AIP and IDH. …”
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The use of sequential multiple assignment randomized trials (SMARTs) in physical activity interventions: a systematic review by Aoife Whiston, K. M. Kidwell, S. O’Reilly, C. Walsh, J. C. Walsh, L. Glynn, K. Robinson, S. Hayes

“…For analyses, most sample size estimations and outcome analyses accounted for the SMART aims specified. Techniques such as linear mixed models, weighted regressions, and Q-learning regression were frequently used. …”
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