Once a linear regression model has been built, it can be used to “score” new data, that is, to estimate the value of the dependent variable in the model using data for which its value may not be known. Scoring is performed using the values of the b-coefficients in the linear regression model and the names of the independent variable columns they correspond to. Other information needed includes the table name(s) in which the data resides, the new table to be created, and primary index information for the new table. The result of scoring a linear regression model will be a new table containing primary index columns and an estimate of the dependent variable, optionally including a residual value for each row, calculated as the difference between the estimated value and the actual value of the dependent variable. The option to include the residual value is available only when model evaluation is requested.
Linear Scoring applies a Linear Regression model to a data set that has the same columns as those used in building the model (with the exception that the scoring input table need not include the predicted or dependent variable column unless model evaluation is requested).