Assigning Data to Roles

To run the Binary Logistic Regression task, you must select an input data source. To filter the input data source, click Filter Icon

. You must also assign columns to the Response variable and a column to either the Classification variables role or the Continuous variables role.

Role	Description
Roles
Response
Response data consists of numbers of events and trials	specifies whether the response data consists of events and trials.
Number of events	specifies the variable that contains the number of events for each observation.
Number of trials	specifies the variable that contains the number of trials for each observation.
Response	specifies the variable that contains the response data. To perform a binary logistic regression, the response variable should have only two levels. Use the Event of interest drop-down list to select the event category for the binary response model.
Link function	specifies the link function that links the response probabilities to the linear predictors. Here are the valid values: Complementary log-log is the complementary log-log function. Probit is the inverse standard normal distribution function. Logit is the log odds function.
Explanatory Variables
Classification variables	specifies the classification variables to use in the analysis. A classification variable is a variable that enters the statistical analysis or model not through its values, but through its levels. The process of associating values of a variable with levels is termed levelization.
Parameterization of Effects
Coding	specifies the parameterization method for the classification variable. Design matrix columns are created from the classification variables according to the selected coding scheme. You can select from these coding schemes: Effects coding specifies effect coding. GLM coding specifies less-than-full-rank, reference-cell coding. This coding scheme is the default. Reference coding specifies reference-cell coding.
Treatment of Missing Values
An observation is excluded from the analysis when either of these conditions is met: if any variable in the model contains a missing value if any classification variable contains a missing value (regardless of whether the classification variable is used in the model)
Continuous variables	specifies the continuous variables to use as the explanatory variables in the analysis.
Additional Roles
Stratify by	specifies the variables that define the strata or matched sets to use in stratified logistic regression of binary response data.
Frequency count	specifies the variables that contain the frequency of occurrence for each observation. The task treats each observation as if it appears n times, where n is the value of the variable for that observation.
Weight variable	specifies the how much to weight each observation in the input data set.
Group analysis by	creates separate analyses based on the number of BY variables.