# 4.9 Summary

4.9 SummaryIn this lesson we learned about:

- The probability density function for the multivariate normal distribution
- The definition of a prediction ellipse
- How the shape of the multivariate normal distribution depends on the variances and covariances
- The definitions of eigenvalues and eigenvectors of a matrix, and how they may be computed
- How to determine the shape of the multivariate normal distribution from the eigenvalues and eigenvectors of the variance-covariance matrix