linear model

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linear model

[′lin·ē·ər ′mäd·əl]
(statistics)
A mathematical model in which linear equations connect the random variables and the parameters. Also known as linear hypothesis.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
References in periodicals archive ?
Table 1: Terms used in linear modeling. The term (y) represents gene expression.
The aim of this paper is to generalize the smart-space domain through linear modeling by using a state equation.
At the same time he analyzed the generalized linear modeling methodology is used in powerful methods involving wider classes of distributions non-linear regression censoring and dependence among responses are required.
Essentially, in the linear modeling it is assumed that the congeneric model holds directly for the observed item scores.
This study investigated gender differences within-families using multilevel linear modeling. Mothers and fathers of children with autism (161 couples) reported on their own wellbeing and their child's functioning.
My topic was using mixed linear modeling to estimate and adjust for environmental effects on competitive performance, and the very short introduction to the method is followed by examples of recent research by my colleagues and students.
Hierarchical linear modeling resolves the problem of misestimated standard errors by incorporating a unique random effect for each institution into the statistical model; moreover, the variability in these random effects is taken into account in estimating the standard errors.
The determination of strength ratios based on knot size can be viewed as a linear modeling problem.
Although they are becoming increasingly important, contemporary methods of applied statistics, including generalized linear modeling, mixed-effects modeling and Bayesian statistical analysis and inference, are not always in the natural resource scientist's toolbag.
Using a 45-parameter model, the NN modeling showed significantly better results than linear modeling when the number of test points was less than 62.
The variance-covariance matrix of the level-2 residuals is referred to in the hierarchical linear modeling literature as [Tau], therefore, element [[Tau].sub.00] represents the variance in [U.sub.0j], element [[Tau].sub.11] represents the variance in [U.sub.1j] and element [[Tau].sub.10] represents the covariance between [U.sub.0j] and [U.sub.ij].
* Complex curvature needs non-linear modeling (see later) or linear modeling with the predictor converted to a nominal variable.