Degree | Type | Year |
---|---|---|
2500149 Mathematics | OB | 3 |
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Linear algebra. Mathematical analysis. Probability.
En este curso es necesario aprender fundamentalmente el concepto de Inferencia.
Se deben introducir y asentar los conceptos de Modelización, Estimación (puntual y por intervalos) y Bondad de ajuste.
Se deben enseñar las técnicas fundamentales de regresión lineal.
Habrá que aprender:
1. La estadística descriptiva y exploratoria que permitirá extraer y resumir de forma eficiente información de los datos.
2. Inferencia estadística: cómo la Estadística cuantifica la incertidumbre de la información extraída de los datos.
3. Se trabajará la modelización de poblaciones, la estimación de parámetros, especialmente máxima verosimilitud, y el planteo y resolución de los contrastes de hipótesis (paramétricos y no-paramétricos) a partir de muestras.
4. Propiedades básicas de estimadores: Invariancia, suficiencia, eficiencia, sesgo, varianza y propiedades asintóticas.
5. Plantear y resolver problemas aplicados. Con los ejemplos, la resolución de problemas y las prácticas con software estadístico, el estudiante trabajará con modelos concretos y datos reales.
The subject is structured in four chapters:
Topic 1: Fundamentals of statistics
Topic 2: Modeling and Basic Inference
Topic 3: Advanced modeling.
Topic 4: Asymptotic laws of estimators and advanced contrasts.
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Master classes: theory | 28 | 1.12 | 10, 3, 7, 5, 2, 13, 11, 12 |
Practical work with computer tools | 14 | 0.56 | 10, 7, 5, 2, 12 |
Problem classes | 14 | 0.56 | 10, 3, 7, 12 |
Type: Supervised | |||
Tutorials | 5 | 0.2 | |
Type: Autonomous | |||
Practical work with computer tools | 25 | 1 | |
Problem solving (workshops and classes) | 20 | 0.8 | 10, 3, 7, 5, 2, 13, 11, 12 |
Study and think problems | 39 | 1.56 | 10, 3, 7, 5, 2, 13, 11, 12 |
We have theoretical, problem and practical classes.
The new material will primarily be introduced in the theory classes, but the teacher's explanations will need to be expanded with the student's independent study, with the support of the reference bibliography. Students' participation in the teacher's exhibitions will be assessed. There will be a partial control of theory and problems in the week of partials designated by the Faculty. Material will be uploaded to the virtual Campus to review the notes taken in class.
The class of problems will be devoted to the oriented resolution of some proposed problems. Students' participation in problem classes will be especially valued.
The practical classes will introduce the use of software such as Excel and/or R with statistical applications. Descriptive and inferential methodologies will be seen to put into practice the concepts worked on in theory and problems.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Computer Exam | 15% | 6 | 0.24 | 1, 10, 3, 9, 8, 7, 6, 2, 11, 12 |
Partial Exam 2 | 40% | 7 | 0.28 | 10, 3, 7, 5, 2, 12 |
Partial Exam-1 | 30% | 5 | 0.2 | 10, 3, 7, 5, 2, 11, 12 |
Problems | 15% | 12 | 0.48 | 1, 10, 4, 3, 9, 7, 2, 13, 11, 12 |
By default, the assessment is carried out continuously throughout the course.
The continuous assessment has several fundamental objectives: Monitor the teaching and learning process, allowing both the student and the teacher to know the degree of achievement of the skills and correct, if possible, the deviations that occur. Encouraging the student's continued effort against over-effort, often useless, at the last minute. Verify that the student has achieved the skills determined in the study plan. That is why it will ask for the accreditation of a minimum level in all assessment activities (a 3 out of 10).
To carry out this evaluation, the following instruments are used: A partial theory test, the documentation provided by the students of their work on problems (problem files), laboratory practice exam, which can be done in a single session or more of one The grade obtained in this assessment represents 60% of the final mark of the subject.
The continuous assessment is complemented by a final written test. The grade thus obtained will represent 40% of the final mark of the subject.
The recovery exam will be addressed to students who, having passed the minimum level, have not yet passed the exam. The part of practices and problems cannot be recovered.
Single assessment: On the date set by the Faculty for the single assessment exam, those taking this modality must hand in a problem file (15%), take a theory and problems exam (with a oral and another written) (70%) and another practical (15%).
BASIC:
COMPLEMENT:
R Core Team (2021). R: A language and environment for statistical computing. R
Foundation for Statistical Computing, Vienna, Austria. URL
https://www.R-project.org/.
Name | Group | Language | Semester | Turn |
---|---|---|---|---|
(PAUL) Classroom practices | 1 | Undefined | second semester | morning-mixed |
(PAUL) Classroom practices | 2 | Catalan | second semester | morning-mixed |
(PLAB) Practical laboratories | 1 | Undefined | second semester | morning-mixed |
(PLAB) Practical laboratories | 2 | Catalan | second semester | morning-mixed |
(TE) Theory | 1 | Catalan | second semester | morning-mixed |