Probability Statistical Inference I Statistical Inference II Regression Methods

General objectives: 1. Capacity for analysis and synthesis. 2. To model and solve problems. 3. Oral and written communication skills. Specific objectives: 1. To know the basic techniques of resampling methods 2. To know and use statistical software to work with resampling techniques.

K4: Know the models and methods of statistical analysis for both static and dynamic data K12: now how to identify or be able to create the statistical or probabilistic model appropriate to the specific problem arising in each business activity (finance, marketing, production planning and control, etc.). C1: Develop and master interpersonal skills on initiative, responsibility, conflict resolution and negotiation, which are essential in the professional environment. C4: Ability to develop and validate statistical models that help to address and solve problems relevant to today's society. C6: Ability to interpret the results of quantitative analysis, prepare clear reports and communicate conclusions effectively, using advanced data analysis tools. S1: To plan and organize team work making the right decisions based on available information and gathering data in digital environments. S5: Manipulate computationally and analytically the established models, taking advantage of the power of statistical methods, optimization, etc., and perform the analysis of the results obtained.

1. Introduction to Computationally Supported Resampling Methods in R 1.1. Resampling Fundamentals: History and Basic Principles 1.2. Computational Tools in R: Introduction to the R Environment and Relevant Statistical Packages 1.3. Types of Resampling: Differences and Applications of Parametric and Nonparametric Bootstrap 2. Introduction to Jackknife and Permutation Tests 2.1. Jackknife Theory: Concepts and Applications 2.2. Permutation Tests: Fundamentals and How to Perform Them in R 3. Concepts Related to Empirical Distribution 3.1. Definition and Properties: What It Is and How to Use It 3.2. Constructing the Empirical Distribution in R: Methods and Functions 3.3. Practical Applications: Using the Empirical Distribution in Data Analysis 4. Estimating Standard Errors and Bias Using Resampling 4.1. Concepts of Standard Error and Bias: Definitions and Relevance 4.2. Resampling Methods for Estimation 4.3. Implementation in R: Practical Examples and Case Studies 5. Resampling in Linear Models and Time Series 5.1. Application in Linear Models: Techniques and Examples in R 5.2. Resampling in Time Series: Challenges and Solutions 6. Confidence Intervals and Hypothesis Testing Based on Resampling 6.1. Constructing Confidence Intervals: Resampling-Based Methods 6.2. Performing Hypothesis Testing: Nonparametric Approaches 6.3. Practical Examples in R: Application to Real-World Datasets 7. Applications in Machine Learning: Bagging and Boosting 7.1. Fundamentals of Bagging: Concepts and How to Implement It in R 7.2. Introduction to Boosting: Theory and Practical Applications 7.3. Advantages of Ensemble Methods: Improving Accuracy and Reducing Overfitting 7.4. Practical exercises: Applying bagging and boosting to machine learning projects

Theory (4 ECTS). Theoretical classes with support material available on the Web. Practice (2 ECTS) problem-solving classes. Computing practices. Presentations and debates.