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.
References in periodicals archive ?
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.
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].
Rutherford's research interests include encoding and retrieval processes in recall and recognition and the effect of mild head injury on cognition, as well as general linear modeling.
His main emphasis is on generalized linear modeling techniques, which extend linear model methods for continuous variables, and their extensions for multivariate responses.
Complex curvature needs non-linear modeling (see later) or linear modeling with the predictor converted to a nominal variable.