Use discriminant analysis to predict group membership for new observations

Usually, Discriminant Analysis is used to calculate the discriminant functions from observations with known groups. When new observations are made, you can use the discriminant function to predict which group they belong to.

If explanatory variables do not follow a multivariate normal distribution with equal covariance matrices for the level of the response, the use of standard discriminant analysis procedures will be statistically inconsistent. In such cases, logistic regression gives more accurate results.

  1. Choose Stat > Multivariate > Discriminant Analysis.
  2. In Groups, enter the column containing the group codes from the original sample.
  3. In Predictors, enter the column(s) containing the measurement data of the original sample.
  4. Click Options. In Predict group membership for, enter constants or columns representing one or more observations. The number of constants or columns must be equivalent to the number of predictors.
  5. Select other dialog box options, as needed, and then click OK.

    You can also calculate (choose Calc > Calculator) the values of the discriminant function for the observation(s) and then assign it to the group with the highest function value.

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