Select the method or formula of your choice.

This content applies to the following tools in Minitab:

- Fit Binary Logistic Model
- Analyze Binary Response for Definitive Screening Design
- Analyze Binary Response for Factorial Design
- Analyze Binary Response for Response Surface Design

The deviance R^{2} indicates how much variation in the response is explained by the model. The higher the R^{2}, the better the model fits your data. The formula is:

Term | Description |
---|---|

D_{E} | Error Deviance |

D_{T} | Total Deviance |

The adjusted deviance R^{2} accounts for the number of predictors in your model and is useful for comparing models with different numbers of predictors. The formula is:

Term | Description |
---|---|

R^{2} | the deviance R^{2} |

p | the regression degrees of freedom |

Φ | 1, for binomial models |

D_{T} | the total deviance |

While the calculations for adjusted deviance R^{2} can produce negative values, Minitab displays zero for these cases.

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 following equation gives the form of the individual contributions for a binomial model:

Term | Description |
---|---|

p | the regression degrees of freedom |

L_{c} | the log-likelihood of the current model |

y_{i} | the number of events for the i^{th} row |

m_{i} | the number of trials for the i^{th} row |

the estimated mean response of the i^{th} row |

AICc is not calculated when .

Term | Description |
---|---|

p | the number of coefficients in the model, including the constant |

n | the number of rows in the data with no missing data |

Term | Description |
---|---|

p | the number of coefficients in the model, not counting the constant |

n | the number of rows in the data with no missing data |