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Univariate and Multivariate General Linear Models: A Comprehensive Guide

Jese Leos
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Published in Univariate And Multivariate General Linear Models: Theory And Applications With SAS Second Edition (Statistics: A Of Textbooks And Monographs)
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Univariate And Multivariate General Linear Models: A Comprehensive Guide Univariate And Multivariate General Linear Models: Theory And Applications With SAS Second Edition (Statistics: A Of Textbooks And Monographs)

General linear models (GLMs) are a fundamental statistical technique used to investigate the relationship between a dependent variable and one or more independent variables. GLMs are particularly useful when the dependent variable is continuous and normally distributed.

Univariate and Multivariate General Linear Models: Theory and Applications with SAS Second Edition (Statistics: A of Textbooks and Monographs)
Univariate and Multivariate General Linear Models: Theory and Applications with SAS, Second Edition (Statistics: A Series of Textbooks and Monographs)
by Kevin Kim

4.9 out of 5

Language : English
File size : 12250 KB
Screen Reader : Supported
Print length : 549 pages
X-Ray for textbooks : Enabled

Univariate GLMs involve a single dependent variable, while multivariate GLMs involve multiple dependent variables. Both univariate and multivariate GLMs can be used to test hypotheses about the effects of the independent variables on the dependent variable(s).

In this article, we will provide a comprehensive overview of univariate and multivariate GLMs. We will discuss their applications, assumptions, and methods for estimation. We will also provide examples of how GLMs can be used to analyze real-world data.

Univariate General Linear Models

Univariate GLMs are used to investigate the relationship between a single dependent variable and one or more independent variables. The dependent variable is typically continuous and normally distributed. The independent variables can be either continuous or categorical.

The general form of a univariate GLM is:

y = β0 + β1x1 + β2x2 + ... + βpxp + ε

where:

* y is the dependent variable * β0 is the intercept * β1, β2, ..., βp are the regression coefficients * x1, x2, ..., xp are the independent variables * ε is the error term

The regression coefficients represent the change in the dependent variable for a one-unit change in the corresponding independent variable, holding all other independent variables constant. The intercept represents the value of the dependent variable when all of the independent variables are equal to zero.

The error term represents the unexplained variation in the dependent variable. It is assumed to be normally distributed with a mean of zero and a constant variance.

Assumptions of Univariate GLMs

The following assumptions must be met for univariate GLMs to be valid:

* The dependent variable is continuous and normally distributed. * The independent variables are fixed and non-random. * The relationship between the dependent variable and the independent variables is linear. * The error term is normally distributed with a mean of zero and a constant variance.

Methods for Estimating Univariate GLMs

The most common method for estimating univariate GLMs is ordinary least squares (OLS). OLS estimates the regression coefficients by minimizing the sum of squared errors between the predicted values of the dependent variable and the observed values of the dependent variable.

Other methods for estimating univariate GLMs include:

* Weighted least squares (WLS) * Generalized least squares (GLS) * Maximum likelihood estimation (MLE)

Applications of Univariate GLMs

Univariate GLMs are used in a wide variety of applications, including:

* Predicting the value of a continuous variable based on a set of independent variables * Testing hypotheses about the effects of independent variables on a continuous variable * Identifying the most important factors that influence a continuous variable

Multivariate General Linear Models

Multivariate GLMs are used to investigate the relationship between multiple dependent variables and one or more independent variables. The dependent variables are typically continuous and normally distributed. The independent variables can be either continuous or categorical.

The general form of a multivariate GLM is:

y = B0 + B1x1 + B2x2 + ... + Bpxp + ε

where:

* y is a vector of dependent variables * B0 is a matrix of intercepts * B1, B2, ..., Bp are matrices of regression coefficients * x1, x2, ..., xp are vectors of independent variables * ε is a vector of error terms

The regression coefficients represent the change in the dependent variables for a one-unit change in the corresponding independent variable, holding all other independent variables constant. The intercepts represent the values of the dependent variables when all of the independent variables are equal to zero.

The error terms represent the unexplained variation in the dependent variables. They are assumed to be multivariate normally distributed with a mean vector of zero and a covariance matrix of Σ.

Assumptions of Multivariate GLMs

The following assumptions must be met for multivariate GLMs to be valid:

* The dependent variables are continuous and multivariate normally distributed. * The independent variables are fixed and non-random. * The relationship between the dependent variables and the independent variables is linear. * The error terms are multivariate normally distributed with a mean vector of zero and a covariance matrix of Σ.

Methods for Estimating Multivariate GLMs

The most common method for estimating multivariate GLMs is generalized least squares (GLS). GLS estimates the regression coefficients by minimizing the generalized sum of squared errors between the predicted values of the dependent variables and the observed values of the dependent variables.

Other methods for estimating multivariate GLMs include:

* Weighted least squares (WLS) * Maximum likelihood estimation (MLE)

Applications of Multivariate GLMs

Multivariate GLMs are used in a wide variety of applications, including:

* Predicting the values of multiple continuous variables based on a set of independent variables * Testing hypotheses about the effects of independent variables on multiple continuous variables * Identifying the most important factors that influence multiple continuous variables

Univariate and multivariate GLMs are powerful statistical techniques that can be used to investigate the relationship between a set of independent variables and a set of dependent variables. GLMs are particularly useful when the dependent variables are continuous and normally distributed.

In this article, we have provided a comprehensive overview of univariate and multivariate GLMs. We have discussed their applications, assumptions, and methods for estimation. We have also provided examples of how GLMs can be used to analyze real-world data.

We encourage you to explore the resources listed below to learn more about GLMs.

Univariate and Multivariate General Linear Models: Theory and Applications with SAS Second Edition (Statistics: A of Textbooks and Monographs)
Univariate and Multivariate General Linear Models: Theory and Applications with SAS, Second Edition (Statistics: A Series of Textbooks and Monographs)
by Kevin Kim

4.9 out of 5

Language : English
File size : 12250 KB
Screen Reader : Supported
Print length : 549 pages
X-Ray for textbooks : Enabled
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The book was found!
Univariate and Multivariate General Linear Models: Theory and Applications with SAS Second Edition (Statistics: A of Textbooks and Monographs)
Univariate and Multivariate General Linear Models: Theory and Applications with SAS, Second Edition (Statistics: A Series of Textbooks and Monographs)
by Kevin Kim

4.9 out of 5

Language : English
File size : 12250 KB
Screen Reader : Supported
Print length : 549 pages
X-Ray for textbooks : Enabled
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