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Permutation tests for univariate or multivariate analysis of variance and regression
, 2001The most appropriate strategy to be used to create a permutation distribution for tests of individual terms in complex experimental designs is currently unclear.
Marti J. Anderson
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2008
Analysis of variance (ANOVA) represents a set of models that can be fit to data, and also a set of methods for summarizing an existing fitted model. We first consider ANOVA as it applies to classical linear models (the context for which it was originally devised; Fisher, 1925) and then discuss how ANOVA has been extended to generalized linear models ...
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Analysis of variance (ANOVA) represents a set of models that can be fit to data, and also a set of methods for summarizing an existing fitted model. We first consider ANOVA as it applies to classical linear models (the context for which it was originally devised; Fisher, 1925) and then discuss how ANOVA has been extended to generalized linear models ...
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2011
Analysis of Variance (ANOVA) is a statistical procedure for comparing means of two or more populations. As the name suggests, ANOVA is a method for studying differences in means by analysis of the variance components in the model. In earlier chapters we have considered two sample location problems; for example, we compared the means of two groups using
Maria L. Rizzo, Jim Albert
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Analysis of Variance (ANOVA) is a statistical procedure for comparing means of two or more populations. As the name suggests, ANOVA is a method for studying differences in means by analysis of the variance components in the model. In earlier chapters we have considered two sample location problems; for example, we compared the means of two groups using
Maria L. Rizzo, Jim Albert
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1981
In earlier chapters we have defined the model linear in the parameters, commonly called the linear model, and discussed the theory of estimating parameters of the model by the method of least squares. Our applications of the theory so far have been to simple linear regression, polynomial regression and multiple regression. However, there is a very wide
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In earlier chapters we have defined the model linear in the parameters, commonly called the linear model, and discussed the theory of estimating parameters of the model by the method of least squares. Our applications of the theory so far have been to simple linear regression, polynomial regression and multiple regression. However, there is a very wide
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1987
In a mean—variance portfolio analysis (Markowitz, 1959) an n-component vector (portfolio) X is called feasible if it satisfies where A is an m x n matrix of constraint coefficients, and b an m-component constant vector. An EV combination is called feasible if for some feasible portfolio.
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In a mean—variance portfolio analysis (Markowitz, 1959) an n-component vector (portfolio) X is called feasible if it satisfies where A is an m x n matrix of constraint coefficients, and b an m-component constant vector. An EV combination is called feasible if for some feasible portfolio.
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1972
Consider the following example, which is a simplified form of a real experiment, with fictitious data.
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Consider the following example, which is a simplified form of a real experiment, with fictitious data.
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Use of Ranks in One-Criterion Variance Analysis
, 1952Given C samples, with n i observations in the ith sample, a test of the hypothesis that the samples are from the same population may be made by ranking the observations from from 1 to Σn i (giving each observation in a group of ties the mean of the ranks
W. Kruskal, W. A. Wallis
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1978
Back in Chapter 5, we learned to use the t test to determine whether or not two means differ significantly from each other. In this chapter, we will learn to use analysis of variance to determine whether or not several means differ significantly from each other.
Samuel T. Mayo, Albert K. Kurtz
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Back in Chapter 5, we learned to use the t test to determine whether or not two means differ significantly from each other. In this chapter, we will learn to use analysis of variance to determine whether or not several means differ significantly from each other.
Samuel T. Mayo, Albert K. Kurtz
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1999
The analysis of variance (or ANOVA ), originally developed by R. A. FISHER, concerns testing the hypothesis of equal means of a number of samples. Such problems occur, for example, in the comparison of a series of measurements carried out under different conditions, or in quality control of samples produced by different machines.
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The analysis of variance (or ANOVA ), originally developed by R. A. FISHER, concerns testing the hypothesis of equal means of a number of samples. Such problems occur, for example, in the comparison of a series of measurements carried out under different conditions, or in quality control of samples produced by different machines.
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