Fit Binary Logistic Modeloverview how to example data see also
Fit Binary Logistic ModelStat > Regression > Binary Logistic Regression > Fit Binary Logistic Model
Use binary logistic regression to perform logistic regression on a binary response variable. A binary variable only has two possible values, such as presence or absence of a particular disease. A model with one or more predictors is fit using an iterative reweighted least squares algorithm to obtain maximum likelihood estimates of the parameters [29].
Binary logistic regression can classify observations into one of two categories. These classifications can give fewer classification errors than discriminant analysis for some cases [10], [31].
The default model contains the variables that you enter in Continuous predictors and Categorical predictors. If you want to add interaction and polynomial terms, use the tools in the Model subdialog box.
Minitab stores the last model that you fit for each response variable. You can use the stored models to quickly generate predictions, contour plots, surface plots, overlaid contour plots, factorial plots, and optimized responses.
See Stored Model Overview for a discussion about how to use stored models.
See Entering data for response variables for examples of response data for each of the three formats: binary response format, frequency format, and event/trial format.
Response in binary response/frequency format: Choose if the response data has been entered as a column that contains 2 distinct values.
Response: Enter the column that contains the response values.
Response event: Choose which event of interest the results of the analysis will describe.
Frequency (optional): If the data are in two columns - one column that contains the response values and one column that contains their frequencies - enter the column that contains the frequencies.
Response in event/trial format: Choose if the response data are two columns - one column that contains the number of successes or events of interest and one column that contains the number of trials.
Event name: Enter a name for the event in the data.
Number of events: Enter the column that contains the number of events.
Number of trials: Enter the column that contains the number of nonevents.
Continuous predictors: Select the continuous variables that explain changes in the response. The predictor is also called the X variable.
Categorical predictors: Select the categorical classifications or group assignments, such as type of raw material, that explain changes in the response. The predictor is also called the X variable.