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However, before we begin our linear regression, we need to recode the values of Male and Female. Why must we do this?

Interpretation The higher the deviance R2, the better the model fits your data. Deviance R2 always increases when you add additional predictors to a model. For example, the best 5-predictor model will always have an R2 that is at least as high as the best 4-predictor model.

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Therefore, deviance R2 is most useful when you compare models of the same size. For binary logistic regression, the format of the data affects the deviance R2 value.

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Deviance R2 values are comparable binar rates between models that use the same data format. Deviance R2 is binar rates one measure of how well the model fits the data. Even when a model has a high R2, you should check the residual plots to assess how well the model fits the data.

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You can use a fitted line plot to graphically illustrate different deviance R2 values. The more deviance that a model explains, the closer the data points fall to the curve.

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Deviance R-Sq adj Adjusted deviance R2 is the proportion of deviance in the response that is explained by the model, adjusted for the number of predictors in the model relative to the number of observations.

Interpretation Use adjusted deviance R2 to compare models that have different numbers of predictors. Deviance R2 always increases when you add a predictor to the model.

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The adjusted deviance R2 value incorporates the number of predictors in the model to help you choose the correct model. For example, you work for a potato chip company that examines factors that affect crumbled potato chips. You receive the following results as you add predictors: Step.

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