Contents: 1. Multivariate normal distribution. 2. Hotelling’s T2 and D2 statistics. 3. Wish art distribution. 4. Discriminant function. 5. Principal component analysis, factor analysis and canonical analysis. 6. Model adequancy in multivariate regression analysis. 7. Multivariate regression in matrix approach. 8. Selecting best regression equation. 9. Examination of residuals in regression analysis. 10. Multicollinearity and reasons for its existence. 11. Sampling distribution of partial and multiple correlation. 12. Elements of stochastic processes.