Degree | Type | Year | Semester |
---|---|---|---|
2503852 Applied Statistics | OB | 3 | 1 |
You can check it through this link. To consult the language you will need to enter the CODE of the subject. Please note that this information is provisional until 30 November 2023.
Knowledge of descriptive and inferential statistics. A previous course of Linear Models is required.
The objective of the subject is to extend the use of linear combinations of a set of predictors to reduce the uncertainty of a response variable. In particular, we will work on the use of parametric models, beyond the normal law, for the response variable. Also, in this more generic modeling environment, we'll go deeper into how to include information, for example, information about the design of the experiment.
Topic 1: Linear models
Simple and multiple linear regression
Extension of linear models and analysis of variance
Fixed and random effects. Introduction to mixed models.
Topic 2: Generalized linear models
Exponential families.
Inference and goodness of fit
Analysis of the models
Topic 3: Classification methods
Introduction of classification methods.
The logistic model. Estimation of regression coefficients.
Multiple logistic regression
The course material (theory notes, lists of problems and statements of practice) will be available at the virtual campus, progressively throughout the course.
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 | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Computer Practices | 50 | 2 | 3, 2, 1, 21, 7, 11, 27, 15, 16, 18, 23, 25, 17, 28 |
Theory | 50 | 2 | 3, 2, 1, 4, 21, 7, 6, 8, 9, 11, 10, 27, 12, 13, 15, 16, 14, 19, 18, 20, 24, 22, 23, 5, 25, 17, 28, 26, 29 |
Type: Supervised | |||
problems / exercises to solve | 16 | 0.64 | 1, 21, 7, 10, 23, 25, 17, 26 |
Type: Autonomous | |||
Preparation for the exam | 10 | 0.4 | 1, 21, 7, 16, 24, 25 |
The subject will be assessed with assignments (exercise assignments, problem checks and/or practicals) and 2 exams. To obtain the weighted grade of continuous assessment you must have a minimum of 3/10 in each of the parts.
Students who have opted for the single assessment modality will have to complete an assessment that will consist of a theory exam, a problem test and the delivery of the first and last practical reports of the course. Assessment of submissions may require an assessment interview with the teacher. The student's grade will be the weighted average of the three previous activities, where the exam will account for 45% of the grade, the test 45% and the assignments 10%.
If the final grade does not reach 5/10, the student has another opportunity to pass the subject through the remedial exam that will be held on the date set by the degree coordinator. In this test you can recover 70% of the grade corresponding to the theory and the problems. The part of internships is not refundable.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Final test | 40% | 4 | 0.16 | 3, 2, 1, 4, 21, 7, 6, 8, 9, 11, 10, 27, 12, 13, 15, 16, 14, 19, 18, 20, 24, 22, 23, 5, 25, 17, 28, 26, 29 |
Partial exam | 30% | 4 | 0.16 | 3, 2, 1, 4, 21, 7, 6, 8, 9, 11, 10, 27, 12, 13, 15, 16, 14, 19, 18, 20, 24, 22, 23, 5, 25, 17, 28, 26, 29 |
Practices (deliveries or check) | 30% | 16 | 0.64 | 1, 21, 7, 11, 27, 15, 16, 18, 24, 22, 23, 25, 17, 26 |
Barndorff-Nielsen, Ole (1978). Information and exponential families in statistical theory. Wiley Series in Probability and Mathematical Statistics. Chichester: John Wiley & Sons, Ltd.
Lee, Y., Nelder, J. and Pawitan, Y. (2006). Generalized Linear Models with Random Effects. Chapman & Hall. London.
John E. Freund, Irwin Miller, Marylees Miller. (2000) Estadística matemática con aplicaciones. Pearson Educación. (existeix castellà)
McCullagh, P. and Nelder, J. (1992). Generalized Linear Models. Chapman & Hall. London.
Pawitan, Y. (2001). In All Likelihood: Statistical Modelling and Inference Using Likelihood. Oxford University Press. Oxford.
Daniel Peña; Regresión y diseño de Experimentos, Alianza Editorial (Manuales de Ciencias Sociales), 2002.
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani; An Introduction to Statistical Learning, Springer texts in Statistics, 2013.
Christopher Hay-Jahans; An R Companion to Linear Statistical Models. Chapman and Hall, 2012.
John Fox and Sandord Weisberg; An R Companion to Applied Regression, 2nd edition, Sage Publications, 2011.
R Core Team. R: A language and environment for statistical computing. R
Foundation for Statistical Computing, Vienna, Austria. URL
https://www.R-project.org/.