Assigning Data to Roles

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

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

Role	Description
Roles
Dependent variable	specifies the numeric variable to use as the dependent variable for the regression analysis. You must assign a numeric variable to this role.
Classification variables	specifies categorical variables that enter the regression model through the design matrix coding.
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 numeric covariates (regressors) for the regression model.
Additional Roles
Frequency count	lists a numeric variable whose value represents the frequency of the observation. If you assign a variable to this role, the task assumes that each observation represents n observations, where n is the value of the frequency variable. If n is not an integer, SAS truncates it. If n is less than 1 or is missing, the observation is excluded from the analysis. The sum of the frequency variable represents the total number of observations.
Weight	specifies the variable to use as a weight to perform a weighted analysis of the data.
Group analysis by	enables you to obtain separate analyses of observations for each unique group.