Results 261 to 270 of about 10,735,998 (316)

Understanding Linear Regression

PM&R, 2013
Multivariate regression is a powerful statistical technique that allows researchers to explore multiple predictors simultaneously, to adjust for confounding, to test for interactions, and to improve predictions. Commonly used regression models include linear regression, logistic regression, and Cox regression.
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extensions in linear regression

Proceedings of the annual conference on - ACM'73, 1973
In regression analysis, the computed equation is the one for which the sum of the squares of the “Absolute Residual Errors” is a minimum. It is very common for the equation to be accepted or rejected on the basis of the magnitude of the “Percent Residual Error” at each data point.
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OLS (Linear) Regression

The Encyclopedia of Research Methods in Criminology and Criminal Justice, 2021
A. L. Burton
semanticscholar   +1 more source

Beyond Multiple Linear Regression

, 2021
P. Roback, J. Legler
semanticscholar   +1 more source

Linear Regression

Business, 2022
S. Aityan
semanticscholar   +1 more source

Detection of Influential Observation in Linear Regression

Technometrics, 2000
R. Cook
semanticscholar   +1 more source

Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models

Computer Speech and Language, 1995
C. Leggetter, P. Woodland
semanticscholar   +1 more source

Regression and the Linear Model

1981
A key feature in most statistical analyses is a statistical model and it will be helpful to look at examples of some simple models, and then discuss some terminology.
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Multiple linear regression

Nature Methods, 2015
Martin, Krzywinski, Naomi, Altman
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Applied Linear Regression Models

, 1983
J. Neter, William Wasserman, M. Kutner
semanticscholar   +1 more source