Interpret the key results for Fit Binary Logistic Model (2024)

To determine how well the model fits your data, examine the statistics in the Model Summary table.

Many of the model summary and goodness-of-fit statistics are affected by how the data are arranged in the worksheet and whether there is one trial per row or multiple trials per row. The Hosmer-Lemeshow test is unaffected by the data format and is comparable between formats. For more information, go to How data formats affect goodness-of-fit in binary logistic regression.

Deviance R-sq

The higher the deviance R², the better the model fits your data. Deviance R² is always between 0% and 100%.

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. Therefore, deviance R² is most useful when you compare models of the same size.

See Also

Logistic regression explained

For binary logistic regression, the format of the data affects the deviance R² value. The deviance R² is usually higher for data in Event/Trial format. Deviance R² values are comparable only between models that use the same data format.

Goodness-of-fit statistics are just one measure of how well the model fits the data. Even when a model has a desirable value, you should check the residual plots and goodness-of-fit tests to assess how well a model fits the data.

Deviance R-sq (adj)

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. The adjusted deviance R² value incorporates the number of predictors in the model to help you choose the correct model.

AIC, AICc, and BIC

Use AIC, AICc, and BIC to compare different models. For each statistic, smaller values are desirable. However, the model with the smallest value for a set of predictors does not necessarily fit the data well. Also use goodness-of-fit tests and residual plots to assess how well a model fits the data.

Area Under ROC Curve

The area under the ROC curve values range from 0.5 to 1. When thebinary model can perfectly separate the classes, then the areaunder the curve is 1. When the binary model cannot separate theclasses better than a random assignment, then the area under thecurve is 0.5.