1.1. Concept and use of Statistics.
1.2. Statistical terms: populations, subpopulations, individuals and samples.
1.3. Types of variables.
2. Analysis of univariate data.
2.1. Representations and graphics of qualitative variables.
2.2. Representations and graphics of quantitative variables.
2.3. Numerical summaries.
3. Analysis of bivariate data.
3.1. Representations and graphics of qualitative and discrete data.
3.2. Representations and numerical summaries of quantitative data: covariance and correlation.
4.1. Random experiments, sample space, elementary and composite events.
4.2. Probability: definition and properties. Conditional Probability and the multiplication Law. Independence.
4.3. The law of total probability and Bayes' theorem.
5. Probability models.
5.1. Random variables. Discrete random variables: Probability function and distribution function. Mean and variance.
5.2. Continuous random variables: Density function and distribution function. Mean and variance.
5.3. Probability models. Discrete probability models: Bernoulli, Binomial and Poisson.
5.4. Continuous probability models: Uniform, exponential and normal.
5.5. Central limit theorem.
6. Introduction to Statistical Inference.
6.1. Point estimation of population parameters.
6.2. Goodness-of-fit of a statistical model. Graphical methods.
6.3. Introduction to confidence interval estimation.