In the case where there are more than two classes, the analysis used in the derivation of the Fisher discriminant can be extended to find a subspace which appears to contain all of the class variability. This generalization is due to C. R. Rao.[8] Suppose that each of C classes has a mean μ i {\displaystyle \mu _{i}} and the same covariance Σ {\displaystyle \Sigma } . Then the scatter between class variability may be defined by the sample covariance of the class means