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Unformatted text preview: But if we add a variable whose t-statistic is less than 1 (in absolute value), then the Adjusted R Squared will decrease. It does balance complexity (more variables in the model) against smaller total squared error. In contrast to every other measure we will see here, we want its maximum rather than its minimum. The AIC The AIC, as Mathematica uses it, appears to be defined as It is more than convenient to use rules at this point, so that I can write symbolic equations involving n, k, and ESS without having Mathematica use the numerical values of them. Let me clear the numerical values… set some rules… then ask for the AIC for the regression (our main one, the Hald data with X1, X2, and X4, which can be found here )… and finally compute it directly using the equation… Additional Selection Criteria...
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- Fall '08
- Akaike information criterion, squared, total squared error, Adjusted R Squared