1. Discrete random vectors.
1.1. Joint, marginal and conditional distributions.
1.2. Independence.
1.3. Functions of random vector.
1.4. Expected value, variance, Conditional expectation.
1.5. Discrete multivariate models.
1.6. Markov inequality. Convergence in probability.
1.7. Laws of Large Numbers
1.8. Moments. generating functions. Convergence in distribution
1.9. Moivre¿Laplace theorem
2. Continuous random vectors.
2.1. Joint, marginal and conditional distributions.
2.2. Independence.
23. Function of random vector.
2.4 Expected value, variance and conditional expectation.
2.6. Markov inequality. Convergence in probability.
2.7. Laws of Large Numbers
2.8. Moments, generating functions. Convergence in distribution
2.9. Central Limit Theorem
3. Distributions related to Normal distribution.
3.1. Change of variable.
3.2. Two-dimensional and multidimensional normal distribution.
3.3. Chi-square and t-student.
3.4. Fisher's theorem