Binary Logistic Regression Minitab Tutorial

Binary Logistic Regression is used to perform logistic regression on a binary response (dependent) variable (a variable only that has two possible values, such as the presence or absence of a particular disease, this kind of variable is known as a dichotomous variable i.e. binary in nature).

Binary Logistic Regression

Binary Logistic Regression can classify observations into one of two categories. These classifications can give fewer classification errors than discriminant analysis for some cases.

The default model contains the variables that you enter in Continuous Predictors and Categorical Predictors. You can also add interaction and/or polynomial terms by using the tools available in the model sub-dialog box.

Minitab stores the last model that you fit for each response variable. These stored models can be used to quickly generate predictions, contour plots, surface plots, overlaid contour plots, factorial plots, and optimized responses.

Minitab Tutorial for Binary Logistic Regression

To perform a Binary Logistic Regression Analysis in Minitab, follow the steps given below. It is assumed that you have already launched the Minitab software.

Step 1:  Choose Stat > Regression > Binary Logistic Regression > Fit Binary Logistic Model.

Step 2:  Do one of the following:

If your data is in raw or frequency form, follow these steps:

1. Choose Response in binary response/frequency format, from the combo box on top
2. In the Response text box, enter the column that contains the response variable.
3. In the Frequency text box, enter the optional column that contains the count or frequency variable.

If you have summarized data, then follow these steps:

1. Choose Response in event/trial format, from the combo box on top of the dialog box.
2. In the Number of events, enter the column that contains the number of times the event occurred in your sample at each combination of the predictor values.
3. In the Number of trials, enter the column that contains the corresponding number of trials.

Step 4:  In Continuous predictors, enter the columns that contain continuous predictors. In Categorical predictors, enter the columns that contain categorical predictors. You can add interactions and other higher-order terms to the model.

Step 5:  If you like, use one or more of the dialog box options, then click OK.

The following are options available in the main dialog box of Minitab Binary Logistic Regression:

• The response in binary response/frequency format: Choose if the response data has been entered as a column that contains 2 distinct values i.e. as a dichotomous variable.
• 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 i.e. one column that contains the response values and the other column that contains their frequencies then 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 the type of raw material, that explain changes in the response. The predictor is also called the X variable.

Step 6: To store diagnostic measures and characteristics of the estimated equation click the Storage… button.

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