In this course, students will delve into advanced concepts of probability in one and multiple variables, exploring in depth the relationships that random variables can have in multivariate stochastic environments. While theoretical tools for interpreting data in complex and high-dimensional settings are provided, students will also be introduced to relevant algorithms for factor analysis and principal component analysis, ANOVA (analysis of variance) across different sample groups, and the analysis of time-related data.
Together with the theory related to these concepts, students will learn to develop and implement all these data analysis techniques using the R programming language and environment.
Titular Professors
Professors
Probability & Statistics
- - Identify statistical and probabilistic methods to develop systems for reasoning, learning, and data manipulation.
- - Recognize and select appropriate data visualization techniques in complex environments.
- - Apply data analysis and visualization methods to effectively communicate relevant results and patterns in data.
1. Multivariant probability
2. Markov chains
3. Factor analysis
4. Analysis of Variance (ANOVA) methods
5. Time series
Each week 5 hours of class will be held, distributed as follows:
- Theory: 2h
- Practice (R): 2h
- Problem-solving: 1h
The course will be evaluated with 2 exams and 1 practical work
The following will be assessed:
- Correct interpretation and handling of data
- Rigor and coherence in the development of reasoning
- Ability to mathematically model basic technical situations
- Accuracy in calculations and proper interpretation of the obtained results
- Clarity and structure in the presentation of procedures and solutions
- Bowerman, B. L., O'Connell, R. T., Murphree, E., Huchendorf, S. C., Porter, D. C., & Schur, P. (2003). Business statistics in practice. New York: McGraw-Hill/Irwin.
- Montgomery, Douglas C., and George C. Runger. Applied statistics and probability for engineers. John wiley & sons, 2010.
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