# Multinomial Logistic Regression

Multinomial Logistic regression is useful for situations in which you want to be able to classify subjects based on values of a set of predictor variables. This type of regression is similar to logistic regression, but it is more general because the dependent variable is not restricted to two categories.

Example. In order to market films more effectively, movie studios want to predict what type of film a moviegoer is likely to see. By performing a Multinomial Logistic Regression, the studio can determine the strength of influence a person's age, gender, and dating status has upon the type of film they prefer. The studio can then slant the advertising campaign of a particular movie toward a group of people likely to go see it.

Statistics. Iteration history, parameter coefficients, asymptotic covariance and correlation matrices, likelihood-ratio tests for model and partial effects, –2 log-likelihood. Pearson and deviance chi-square goodness of fit. Cox and Snell, Nagelkerke, and McFadden R 2. Classification: observed versus predicted frequencies by response category. Crosstabulation: observed and predicted frequencies (with residuals) and proportions by covariate pattern and response category.

Methods. A multinomial logit model is fit for the full factorial model or a user-specified model. Parameter estimation is performed through an iterative maximum-likelihood algorithm.

## Multinomial Logistic Regression data considerations

Data. The dependent variable should be categorical. Independent variables can be factors or covariates. In general, factors should be categorical variables and covariates should be continuous variables.

Assumptions. It is assumed that the odds ratio of any two categories are independent of all other response categories. For example, if a new product is introduced to a market, this assumption states that the market shares of all other products are affected proportionally equally. Also, given a covariate pattern, the responses are assumed to be independent multinomial variables.

## Obtaining a Multinomial Logistic Regression

This feature requires SPSS® Statistics Standard Edition or the Regression Option.

1. From the menus choose:

Analyze > Regression > Multinomial Logistic Regression...

2. Select one dependent variable.
3. Factors are optional and can be either numeric or categorical.
4. Covariates are optional but must be numeric if specified.

This procedure pastes NOMREG command syntax.