## Introduction

The Multinomial Logit is a form of regression analysis that models a discrete and nominal dependent variable with more than two outcomes (Yes/No/Maybe, Red/Green/Blue, Brand A/Brand B/Brand C, etc.).

This article describes how to create a Multinomial Logit output as shown below. The example below is a model that predicts a survey respondent’s brand choice based on characteristics like age, gender, and work status.

## Requirements

- An
**Outcome**variable with more than two outcomes to be predicted. Ideally, a*Nominal: Mutually exclusive categories*variable. When using stacked data the**Outcome**variable should be a single question in a Multi type structure. **Predictors**variables will be considered as predictors of the outcome variable. When using stacked data the*Predictor(s)*need to be a single question in a Grid type structure.

## Method

- Go to
**Anything > Advanced Analysis >****Regression > Multinominal Logit.** - In the
**object inspector**go to the**Inputs**tab. - In the
**Output**menu select the 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.

- 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. - OPTIONAL: Select
**Random seed**to initialize the (pseudo)random number generator for the model fitting algorithm. Different seeds may lead to slightly different answers, but should normally not make a large difference.

## See Also

How to Run Binary Logit Regression

How to Run a Generalized Linear Model

How to Run NBD Regression in Displayr

How to Run Ordered Logit Regression

How to Run Quasi-Poisson Regression

How to Run a Stepwise Regression

How to Create Regression Multicollinearity Table (VIF)

How to Create a Prediction-Accuracy Table

How to Create a Goodness-of-Fit Plot

How to Test Residual Heteroscedasticity of Regression Models

How to Save Predicted Values of Regression Models

How to Save Fitted Values of Regression Models

How to Save Probabilities of Each Response of Regression Models

How to Test Residual Normality (Shapiro-Wilk) of Regression Models

How to Test Residual Serial Correlation (Durbin-Watson) of Regression Models

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