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Visual Representation and the Prediction of Emotion
Unformatted Document Text:  25 Appendix A Generalized Linear Regression Use of the generalized linear regression model is recommended when there is violation of the assumption that the residuals in the regression equation are independent and uncorrelated. If it is the case that when one residual is small or large, others are likely to be small or large, the sum squared of the residuals is weighted in a way that accounts for these associations. So, for example, in generalized linear regression, an observation that would be expected to have a large residual because other observations have large residuals is given a smaller weight, and a large weight is given to associated small residuals. We selected this procedure because individual subjects contributed more than one data point to the final set of responses. We would anticipate that responses from the same subjects might be associated. Generalized Least Squares regression helps to fix this violation of the assumption of independence.

Authors: Sherr, Susan.
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25
Appendix A

Generalized Linear Regression
Use of the generalized linear regression model is recommended when
there is violation of the assumption that the residuals in the regression equation
are independent and uncorrelated. If it is the case that when one residual is small
or large, others are likely to be small or large, the sum squared of the residuals is
weighted in a way that accounts for these associations. So, for example, in
generalized linear regression, an observation that would be expected to have a
large residual because other observations have large residuals is given a smaller
weight, and a large weight is given to associated small residuals.
We selected this procedure because individual subjects contributed more
than one data point to the final set of responses. We would anticipate that
responses from the same subjects might be associated. Generalized Least Squares
regression helps to fix this violation of the assumption of independence.


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