Comparison of Artificial Neural Network and Multiple Linear Regression to Predict Cadmium Concentration in Rice: A Field Study in Guangxi, China. [PDF]
ToxicsZhao J +6 more
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The Detection of Influential Subsets in Linear Regression by Using an Influence Matrix [PDF]
, 1995 Daniel Peña, Vı́ctor J. Yohai
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Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Olfactory dysfunction is frequently observed in patients with multiple sclerosis (MS); however, its underlying mechanisms remain poorly understood. To date, no studies have directly examined the nasal mucosal microbiota in MS. This study aimed to explore potential relationships among olfactory function, nasal microbiota composition,
Zidan Gao +5 more
wiley +1 more source
A hybrid approach for forecasting peak expiratory flow rate in asthma patients using combined linear regression and random forest model. [PDF]
PLoS OneAlkobaisi S +6 more
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Characteristics of Cerebral Palsy in the Midwestern US
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Cerebral palsy (CP) is the most common lifelong motor disability worldwide. Yet, data is limited on how CP manifests in the US. Our objective was to characterize and determine factors affecting functional outcomes in a large population of young people with CP in the Midwestern US.
Susie Kim +6 more
wiley +1 more source
Multiple linear regression model was constructed based on the influencing factors of vancomycin trough concentration. [PDF]
Dose ResponseWang L +5 more
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The Separation Principle in Linear Regression [PDF]
, 1997 Francisco J. Samaniego, Mitchell Watnik
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Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Managing long COVID in people with multiple sclerosis and related disorders (pwMSRD) is complex due to overlapping symptoms. To address evidence gaps, we evaluated long COVID susceptibility in pwMSRD versus controls and its associations with multi‐domain function and disability.
Chen Hu +15 more
wiley +1 more source
To interact or not to interact: The pros and cons of including interactions in linear regression models. [PDF]
Behav Res MethodsRimpler A, Kiers HAL, van Ravenzwaaij D.
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Robust minimax designs for multiple linear regression
, 1990 Douglas P. Wiens
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