The objective of the course is that the student learns basic statistical concepts and tools that will allows him to: a) analyze, summarize, and draw conclusions from real world data and b) understand the concepts of uncertainty and probability and apply distribution models to solve relevant problems.
1. Introduction
1.1. Concepts 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. Introduction to Probability
4.1. Introduction
4.2. Random phenomena
4.3. Definition of probability and properties
4.4. Assessment of probabilities in practice
4.5. Conditional probability
4.6. Bayes Theorem
5. Random variables
5.1. Definition of random variable
5.2. Discrete random variables
5.3. Continuous random variables
5.4. Characteristic features of a random variable
5.5. Random vectors
5.6. Independence of random variables
6. Distribution models
6.1. Binomial distribution
6.2. Geometric distribution
6.3. Poisson distribution
6.4. Uniform distribution (continuous)
6.5. Exponential distribution
6.6. Normal distribution (with CLT)
7. Linear regression
7.1. Introduction
7.2. Simple linear regression
7.3. Multiple linear regression
