Minitab provides three link functions, which let you fit a broad variety of binary response models. A link function is the inverse of a distribution function. You want to choose a link function that results in a good fit to your data. Examine the goodness-of-fit statistics in the output to compare how well the model fits your data using different link functions. You can also choose link functions for historical reasons or because they have a special meaning in your discipline. For more information, go to What is a link function?.

- Logit: By default, Minitab uses the logit link function because it provides the most natural interpretation of the estimated coefficients and it provides estimates of the odds ratios.
- Normit/Probit: Use the normit link function, which assumes that there is an underlying variable that follows a normal distribution that is classified into categories. For example, you assume that pesticide resistance is an unmeasurable characteristic of an insect that follows a normal distribution. However, instead of recording pesticide resistance, you classify insects into categories of survived and died at different doses of pesticide.
- Gompit/Complementary log-log: Use the gompit function, which is the inverse of the Gompertz distribution function. If the logit or normit functions do not fit the data, the gompit function can sometimes provide an adequate fit because the gompit function is asymmetric.

Enter the level of confidence for the confidence interval that is displayed on the fitted line plot.

Usually, a confidence level of 95% works well. A 95% confidence level indicates that, if you took 100 random samples from the population, the confidence intervals for approximately 95 of the samples would contain the parameter that the interval estimates. For a given set of data, a lower confidence level produces a narrower interval, and a higher confidence level produces a wider interval.

To display the confidence interval, you must go to the Graphs subdialog box, and under Binary fitted line plot, select Display confidence interval.

You can select a two-sided interval or a one-sided bound. For the same confidence level, a bound
is closer to the point estimate than the interval. The upper bound does not
provide a likely lower value. The lower bound does not provide a likely
upper value.

- Two-sided
- Use a two-sided confidence interval to estimate both likely lower and upper values for the mean response.
- Lower bound
- Use a lower bound to estimate a likely lower value for the mean response.
- Upper bound
- Use an upper confidence bound to estimate a likely higher value for the mean response.

Enter a value to change the units for calculating the odds ratio from the units in the data. For example, if the predictor is mass in grams, enter 1000 to determine the change in the odds ratio for a kilogram.