1. Discrete random vectors.
1.1. Joint, marginal and conditional distributions.
1.2. Independence.
1.3. Functions of random vectors.
1.4. Expected value and variance, conditional expectation.
1.5. Discrete multivariate models.
2. Continuous Random vectors.
2.1. Joint, marginal and conditional distributions.
2.2. Independence. Functions of random vectors.
2.3. Change of variable.
2.4. Expected value and variance, conditional expectation.
2.4. Bimensional normal distribution.
3. Introduction to statistical inference.
3.1.Simple random samples.
3.2. Markov inequality and weak law of large numbers.
3.3. Central Limit Theorem.
3.4. Sampling distributions based on normal populations (chi-square and Student t test).
3.5. Confidence intervals.