GLM Example 1: Logistic Regression Analysis with Intercept - Teradata Vantage

In logistic regression, the dependent variable (Y) has only two possible values (0 and 1, 'yes' and 'no', or 'true' and 'false'). The algorithm applies the model to the data and predicts the most likely outcome.

Input

The input table, admissions_train, contains data about applicants to an academic program. For each applicant, attributes in the table include a Masters Degree indicator, a grade point average (on a 4.0 scale), a statistical skills indicator, a programming skills indicator, and an indicator of whether the applicant was admitted. The Masters Degree, statistical skills, and programming skills indicators are categorical variables. Masters degree has two categories (yes or no), while the other two have three categories (Novice, Beginner and Advanced). For admitted status, 1 indicates that the student was admitted and 0 indicates otherwise.

SQL Call

The default option is to include the intercept with the step argument set to false. The response variable (admitted, in this example) must be specified as the first variable listed in the InputColumns argument, followed by the other predictors.

DROP TABLE glm_admissions_model;

SELECT * FROM GLM (
  ON admissions_train AS InputTable
  OUT TABLE OutputTable (glm_admissions_model)
  USING
  InputColumns ('admitted','masters', 'gpa', 'stats', 'programming')
  CategoricalColumns ('masters', 'stats', 'programming')
  Family ('LOGISTIC')
  LinkFunction ('LOGIT')
  WeightColumn ('1')
  StopThreshold (0.01)
  MaxIterNum (25)
  Step ('false')
  Intercept ('true')
) AS dt;

Output

The output table shows the model statistics.

For categorical variables, the model selects a reference category. This example uses the Advanced category as a reference for the stats variable.

This query returns the following table:

SELECT * FROM glm_admissions_model ORDER BY attribute;