Deviance R 2 is always between 0% and 100%.ĭeviance R 2 always increases when you add additional predictors to a model.
The higher the deviance R 2, the better the model fits your data. For more information, go to For more information, go to How data formats affect goodness-of-fit in binary logistic regression. For binary logistic regression, the data format affects the deviance R 2 statistics but not the AIC. To determine how well the model fits your data, examine the statistics in the Model Summary table. For more information on removing terms from the model, go to Model reduction. If there are multiple predictors without a statistically significant association with the response, you must reduce the model by removing terms one at a time. You may want to refit the model without the term. P-value > α: The association is not statistically significant If the p-value is greater than the significance level, you cannot conclude that there is a statistically significant association between the response variable and the term. P-value ≤ α: The association is statistically significant If the p-value is less than or equal to the significance level, you can conclude that there is a statistically significant association between the response variable and the term. A significance level of 0.05 indicates a 5% risk of concluding that an association exists when there is no actual association. Usually, a significance level (denoted as α or alpha) of 0.05 works well. The null hypothesis is that the term's coefficient is equal to zero, which indicates that there is no association between the term and the response. To determine whether the association between the response and each term in the model is statistically significant, compare the p-value for the term to your significance level to assess the null hypothesis.