The

*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 Outputs

How to Interpret Logistic Regression Coefficients

How to Do an Ordered Logit in Q

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