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**Important:** When using AIC and BIC to compare models, the same dataset should be used for both models. This becomes relevant when some cases are excluded from a model due to missing values on some of the variables.
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### 4. Local Measures of Fit |
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### 4. Local Measures of Fit
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local measures of fit deals with the observations which are influential. Models may represent most of the data well, except for a subset of observations. This influential observations can affect the goodness-of-fit of the model to the data or estimated parameters.
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With respect to goodness-of-fit of the model to the data, standardized residuals can be examined (e.g. Pearson residuals or deviance residuals). These residuals should be normally distributed. Also adjusted residuals can be computed which should be distributed as <img src="https://latex.codecogs.com/svg.latex?N(0,1)" title="N(0,1)" />. To find influential observation, we can exclude an observation and re-calculate the statistic for the others. If the statistic is significantly different from the statistic that was calculated including the observation, the observation would be an influential observation. These can be considered as the outliers of the dataset. |
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