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Analysis of Neutrosophic Multiple Regression [PDF]
Neutrosophic Sets and Systems, 2021 The idea of Neutrosophic statistics is utilized for the analysis of the uncertainty observation data. Neutrosophic multiple regression is one of a vital roles in the analysis of the impact between the dependent and independent variables. The Neutrosophic
D. Nagarajan +3 more
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Regression analysis of expanded polystyrene properties [PDF]
Mathematics in Education, Research and Applications, 2020 Own measurements examine the tensile strength of expanded polystyrene (EPS) depending on its bulk density. 30 samples were used to calculate the correlation coefficients between these two properties.
Dušan Páleš, Milada Balková
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Does COVID-19-specific news affect stock market liquidity? Evidence from Japan
MethodsX, 2023 This article examines the effect of COVID-19-specific news on stock market liquidity in the Japanese Topix 500-listed firms. Our empirical analyses show that both COVID-19 confirmed cases and COVID-19-specific news induce a negative effect on stock ...
Wurong Yang +2 more
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Rheumatology, 2023 Introduction Lyme borreliosis (LB) is a multisystemic zoonotic disease transmitted by the bite of infected tick vectors. The aim of the study is to develop a mathematical model for predicting the risk of severity of Lyme disease by the risk factor of ...
Svetlana oleksiivna Nykytyuk +4 more
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R-environment package for regression analysis [PDF]
Pesquisa Agropecuária Brasileira, 2018 : The objective of this work was to develop a package in the R environment for automating and facilitating the regression analysis. Named easyreg, the package offers five functions.
Emmanuel Arnhold
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Latent regression analysis [PDF]
Statistical Modelling, 2010 Finite mixture models have come to play a very prominent role in modelling data. The finite mixture model is predicated on the assumption that distinct latent groups exist in the population. The finite mixture model therefore is based on a categorical latent variable that distinguishes the different groups.
Tarpey, Thaddeus, Petkova, Eva
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Missing Data Analysis in Regression
Applied Artificial Intelligence, 2022 Many of the datasets in real-world applications contain incompleteness. In this paper, we approach the effects and possible solutions to incomplete databases in regression, aiming to bridge a gap between theoretically effective algorithms.
C. G. Marcelino +3 more
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, 2023 Nonlinear regression analysis is a popular and important tool for scientists and engineers. In this article, we introduce theories and methods of nonlinear regression and its statistical inferences using the frequentist and Bayesian statistical modeling and computation.
Huang, Hsin-Hsiung, He, Qing
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Psychiatry and Behavioral Sciences, 2013 Linear regression is an approach to modeling the association between a numeric dependent variable y and one or more independent variables denoted X. The case of one explanatory variable in regression model is called simple linear regression.
Selim Kılıc
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Regression analysis of ionospheric disturbance factors [PDF]
E3S Web of Conferences, 2020 Investigation of interactions of the near-planet space parameters, Earth magnetic field and ionospheric parameters are of interest in the tasks of solar-terrestrial physics and applied researches related to space weather.
Polozov Yuryi, Mandrikova Oksana
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