Binary Logit^{} is a form of regression analysis that models a binary dependent variable (e.g. yes/no, pass/fail, win/lose). It is also known as a Logistic regression and Binomial regression.
This article describes how to create a Binary Logit Regression output as shown below. The example below is a model that predicts a survey respondent’s likelihood of having consumed a fast-food product based on characteristics like age, gender, and work status.
Requirements
- The key requirement for a binary logit regression is that the dependent variable is binary. In Q, the best data format for this type is “Nominal: Mutually exclusive categories”, with values of “0” and “1”.
- The independent variables can be continuous, categorical, or binary — just as with any other regression model.
Method
- Go to Create > Regression > Binary Logit.
- In the object inspector go to the Inputs tab.
- In the Output menu select the binary variable to be predicted by the predictor variables.
- Select the predictor variable(s) from the Predictor(s) list.
- OPTIONAL: Select the desired Output type:
- Summary: The default; as shown in the example above.
- Detail: Typical R output, some additional information compared to Summary, but without the pretty formatting.
- ANOVA: Analysis of variance table containing the results of Chi-squared likelihood ratio tests for each predictor.
- Relative Importance Analysis: The results of a relative importance analysis.
- Effects Plot Plots the relationship between each of the Predictors and the Outcome.
- OPTIONAL: Select the desired Missing Data treatment. (See Missing Data Options).
- OPTIONAL: Select Variable names to display variable names in the output instead of labels.
- OPTIONAL: Select Correction. Choose between None (the default), False Discovery Rate, Bonferroni.
- OPTIONAL: Specify the Automated outlier removal percentage to remove possible outliers.
- OPTIONAL: Select Stack data to stack the input data prior to analysis. Stacking can be desirable when each individual in the data set has multiple cases and an aggregate model is desired.
Next
How to Interpret Logistic Regression Coefficients