# 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.

- From the menus choose:
- Select one dependent variable.
- Factors are optional and can be either numeric or categorical.
- Covariates are optional but must be numeric if specified.

This procedure pastes NOMREG command syntax.