نتایج جستجو برای: multiple linear regression models

تعداد نتایج: 2130516  

Journal: :فیزیک زمین و فضا 0
مرتضی عرفانیان نوروززاده دانشگاه تهران وحید ابراهیم زاده اردستانی هیأت علمی بخش گرانی سنجی-مؤسسه ژئوفیزیک دانشگاه تهران

in this paper, a new formula is applied for the calculation of gravity anomalies from a cylinder model representing a geological body. compared to conventional methods, this new development allows the cylinder to be freely oriented in space. 38 gravity forward models are produced each of which anomaly attributes are calculated. then a linear relationship is established between attributes and so...

Journal: :Computational Statistics & Data Analysis 2017

Journal: :Computational Statistics & Data Analysis 2020

Journal: :Journal of Agrometeorology 2022

Wheat yield production is largely attributed by weather parameters. Model developed multiple linear, neural network and penalised regression techniques using data have the potential to provide reliable, timely cost-effective prediction of wheat yield. parameter during crop growing period (46th 15th SMW) for more than 30 years were collected study area model was stepwise linear (SMLR), principal...

Journal: :iranian journal of mathematical chemistry 2016
f. bagheban-shahri a. niazi a. akrami

a quantitative structure-activity relationship (qsar) study was conducted for the prediction of inhibitory activity of 1-phenyl[2h]-tetrahydro-triazine-3-one analogues as inhibitors of 5-lipoxygenase. the inhibitory activities of the 1-phenyl[2h]-tetrahydro-triazine-3-one analogues modeled as a function of molecular structures using chemometrics methods such as multiple linear regression (mlr) ...

2001
Philippe BASTIEN Michel TENENHAUS

Problems encountered in multiple regression due to multicolinearity or missing data can be overcome by using PLS regression. Several versions of the PLS regression algorithm exist. In this paper, we present a new version of this algorithm which can be extended to generalized linear regression models such as ordinal or multinomial logistic regression, generalized linear models, and Cox regressio...

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