Logistic regression is used to study the relationship between a response variable and its predictor(s), where the response variable is categorical. Nominal Logistic Regression is used to perform logistic regression on a response variable that has 3 or more levels and these levels have no natural ordering. Examples of an nominal variable is such as North/South/East/West and Blue/Red/Green/Yellow.
Response variables and terms must be presented in columns. Covariates must be numeric and factors must have at least 2 distinct levels. The Model can include factors, covariates, interactions and nested term. Multiple terms must be separated by commas. Use * to represent interactions and () to represent nesting. The model must be hierachical, if an interaction term is included, all lower order interactions and main terms must also be included.
ProcessMA uses maximum likelihood estimates to model parameters.
At the Excel Menu (For Excel 2007, go to Add-ins first)
Choose ProcessMA > Statistics > Regression > Nominal Logistic Regression
In Response, select the column containing the response data
In Term 1 to Term 10 , select the column containing data for the respective model terms and choose if the term is a covariate or a factor (Covariate: Numeric)
In Model, enter the terms to be included in the model.
In Event, choose the reference event of the response
Click OK
You want to understand the type of sports people like to play and how is this associated with their gender and age.
Open data worksheet by choosing ProcessMA > Tools > Data
Choose ProcessMA > Statistics > Regression > Nominal Logistic Regression
In Response, select AN - Sports
In Term 1, select AO - Gender and choose Factor
In Term 2, select AP - Age and choose Covariate
In Model, enter Gender, Age
In Event, choose Golf
Click OK
From the logistic regression table, you can obtain the coefficients for the regression model. There are two sets of parameter estimates, Squash and Tennis as compared to Golf.
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