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Dimethyl Fumarate, But Not Rituximab, Reduces Serum GFAP Levels and PIRMA in Relapsing–Remitting MS
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Serum neurofilament light chain (sNfL) and glial fibrillary acidic protein (sGFAP) levels are believed to reflect mainly acute and chronic disease processes in multiple sclerosis (MS), respectively. In this study, we investigated whether dimethyl fumarate (DMF) and rituximab (RTX) differentially affect these biomarkers.
F. Shawket +14 more
wiley +1 more source
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Residuals in Multiple Regression Analysis
Journal of Pharmaceutical Sciences, 1985An examination of residuals can be used as a technique to determine the best model for use in multiple-regression analysis. In addition to R2 and the Fisher F ratio, a complete analysis of residuals has been used by statisticians for many years to estimate bias and to choose the best equation to fit physicochemical as well as biological and ...
Y J, Jeng, A, Martin
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Regression analysis of multiple protein structures
Proceedings of the second annual international conference on Computational molecular biology - RECOMB '98, 1998A general framework is presented for analyzing multiple protein structures using statistical regression methods. The regression approach can superimpose protein structures rigidly or with shear. Also, this approach can superimpose multiple structures explicitly, without resorting to pairwise superpositions.
T D, Wu +3 more
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2020
This chapter describes the multiple linear regression by a nontechnical language and simple examples. Section 1.1 shows how this technique can recreate the real relationships between the variables (phenomena) as a regression equation. It illustrates how the dependent variable (effect) is related to each of the independent variables (causes) as ...
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This chapter describes the multiple linear regression by a nontechnical language and simple examples. Section 1.1 shows how this technique can recreate the real relationships between the variables (phenomena) as a regression equation. It illustrates how the dependent variable (effect) is related to each of the independent variables (causes) as ...
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Regression analysis of multiple outcomes. [PDF]
Epidemiologia e prevenzione, 2001 In this paper we present an extension of the regression models to more than one response and more than one group of covariates, in a way that purely explanatory and intermediate variables can be modelled simultaneously. While we limit our examplification to continuous gaussian data, the method discussed is quite general and allows continuous ...
Biggeri A. +2 more
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Multiple regression analysis of twin data
Behavior Genetics, 1985A multiple regression model for the analysis of twin data is described in which a cotwin's score is predicted from a proband's score and the coefficient of relationship (R=1.0 and 0.5 for identical and fraternal twin pairs, respectively). This model is especially appropriate for the analysis of data on twins in which one member of each pair has been ...
J C, DeFries, D W, Fulker
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Thematic Map Analysis Using Multiple Regression
Mathematical Geology, 2001zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Brower, James C., Merriam, Daniel F.
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1998
So far we have considered only one regressor X besides the constant in the regression equation. Economic relationships usually include more than one regressor. For example, a demand equation for a product will usually include real price of that product in addition to real income as well as real price of a competitive product and the advertising ...
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So far we have considered only one regressor X besides the constant in the regression equation. Economic relationships usually include more than one regressor. For example, a demand equation for a product will usually include real price of that product in addition to real income as well as real price of a competitive product and the advertising ...
openaire +1 more source
On multiple regression analysis
Statistica Neerlandica, 1962SummaryThe sums of squares associated with the independent variables in a multiple regression equation depend on the order in which these variables are introduced. Two methods have been proposed in the literature to avoid this inconvenience: “forward selection” or “backward elimination”.With forward selection the independent variables are introduced in
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