BookDOI
Plane answers to complex questions: the theory of linear models
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In this article, one-way ANOVA and multiple comparison techniques were used to estimate the covariance of a model with a multifactor analysis of Variance component in a design model.Abstract:
Introduction.- Estimation.- Testing.- One-Way ANOVA.- Multiple Comparison Techniques.- Regression Analysis.- Multifactor Analysis of Variance.- Experimental Design Models.- Analysis of Covariance.- General Gauss-Markov Models.- Split Plot Models.- Mixed Models and Variance Components.- Model Diagnostics.- Variable Selection.- Collinearity and Alternative Estimates.-read more
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Journal ArticleDOI
Permutation inference for the general linear model.
Anderson M. Winkler,Anderson M. Winkler,Anderson M. Winkler,Gerard R. Ridgway,Matthew A. Webster,Stephen M. Smith,Thomas E. Nichols +6 more
TL;DR: This paper presents a generic framework for permutation inference for complex general linear models (glms) when the errors are exchangeable and/or have a symmetric distribution, and shows that, even in the presence of nuisance effects, these permutation inferences are powerful while providing excellent control of false positives in a wide range of common and relevant imaging research scenarios.
Journal ArticleDOI
About regression-kriging: From equations to case studies
TL;DR: This paper discusses the characteristics of regression-kriging (RK), its strengths and limitations, and illustrates these with a simple example and three case studies, and addresses pragmatic issues: implementation of RK in existing software packages, comparison ofRK with alternative interpolation techniques, and practical limitations to using RK.
Journal ArticleDOI
Reversal of Left Ventricular Hypertrophy in Essential Hypertension: A Meta-analysis of Randomized Double-blind Studies
TL;DR: In this first meta-analysis including only double-blind, randomized, controlled clinical studies, decline in blood pressure, duration of drug treatment, and drug class determined the reductions in left ventricular mass index.
Journal ArticleDOI
GSTAT: a program for geostatistical modelling, prediction and simulation
Edzer Pebesma,C. G. Wesseling +1 more
TL;DR: The class of problems gstat can solve is described, and aspects of efficiency and implementation, managing geostatistical projects, and relevant technical details are addressed.
Journal ArticleDOI
Hormones and menopausal status as predictors of depression in women in transition to menopause.
TL;DR: Hormone associations provided corroborating evidence that the changing hormonal milieu contributes to dysphoric mood during transition to menopause and decreased in postmenopausal women.
References
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Book
Generalized Linear Models
Peter McCullagh,John A. Nelder +1 more
TL;DR: In this paper, a generalization of the analysis of variance is given for these models using log- likelihoods, illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables), and gamma (variance components).
Book
Applied Regression Analysis
Norman R. Draper,Harry Smith +1 more
TL;DR: In this article, the Straight Line Case is used to fit a straight line by least squares, and the Durbin-Watson Test is used for checking the straight line fit.
Journal ArticleDOI
An Analysis of Variance Test for Normality (Complete Samples)
S. S. Shapiro,M. B. Wilk +1 more
TL;DR: In this article, a new statistical procedure for testing a complete sample for normality is introduced, which is obtained by dividing the square of an appropriate linear combination of the sample order statistics by the usual symmetric estimate of variance.
Journal ArticleDOI
An Analysis of Transformations
George E. P. Box,David Cox +1 more
TL;DR: In this article, Lindley et al. make the less restrictive assumption that such a normal, homoscedastic, linear model is appropriate after some suitable transformation has been applied to the y's.
Book
Applied Multivariate Statistical Analysis
R. A. Johnson,Dean W. Wichern +1 more
TL;DR: In this article, the authors present an overview of the basic concepts of multivariate analysis, including matrix algebra and random vectors, as well as a strategy for analyzing multivariate models.