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2009
The general linear model incorporates many of the most popular and useful models that arise in applied statistics, including models for multiple regression and the analysis of variance. The basic model can be written succinctly in matrix form as $$Y = X\beta + \epsilon,$$ (14.1) where Y, our observed data, is a random vector in \(\mathbb{R}^n,
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The general linear model incorporates many of the most popular and useful models that arise in applied statistics, including models for multiple regression and the analysis of variance. The basic model can be written succinctly in matrix form as $$Y = X\beta + \epsilon,$$ (14.1) where Y, our observed data, is a random vector in \(\mathbb{R}^n,
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1998
Abstract The data in Table 4.1 are pairs of measurements of body weights and diastolic blood pressures recorded on 34 heavy men. Even with as few as 34 observations, it is useful to summarize the relationship between two variables.
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Abstract The data in Table 4.1 are pairs of measurements of body weights and diastolic blood pressures recorded on 34 heavy men. Even with as few as 34 observations, it is useful to summarize the relationship between two variables.
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IEEE Transactions on Neural Networks and Learning Systems, 2020
Min Yang, Yunong Zhang, Haifeng Hu
exaly
Min Yang, Yunong Zhang, Haifeng Hu
exaly
Statistical parametric maps in functional imaging: A general linear approach
Human Brain Mapping, 1994K J Friston
exaly