Methods and formulas for the model summary in Binary Fitted Line Plot

Select the method or formula of your choice.

Deviance R2

The deviance R2 indicates how much variation in the response is explained by the model. The higher the R2, the better the model fits your data. The formula is:

Notation

TermDescription
DEError Deviance
DTTotal Deviance

Adjusted Deviance R2

The adjusted deviance R2 accounts for the number of predictors in your model and is useful for comparing models with different numbers of predictors. The formula is:

Notation

TermDescription
R2the deviance R2
pthe regression degrees of freedom
Φ1, for binomial and Poisson models
DTthe total deviance

While the calculations for adjusted deviance R2 can produce negative values, Minitab displays zero for these cases.

Akaike Information Criterion (AIC)

Use this statistic to compare different models. The smaller AIC is, the better the model fits the data.

The log-likelihood functions are parameterized in terms of the means. The general form of the functions follow:

The general form of the individual contributions follows:

The specific form of the individual contributions depends on the model.

Model li
Binomial
Poisson

Notation

TermDescription
pthe regression degrees of freedom
Lcthe log-likelihood of the current model
yithe number of events for the ith row
mithe number of trials for the ith row
the estimated mean response of the ith row
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