2023/2024

Degree | Type | Year | Semester |
---|---|---|---|

2503852 Applied Statistics | OB | 3 | 1 |

- Name:
- Isabel Serra Mochales
- Email:
- isabel.serra@uab.cat

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.

- Analyse data using statistical methods and techniques, working with data of different types.
- Correctly use a wide range of statistical software and programming languages, choosing the best one for each analysis, and adapting it to new necessities.
- Critically and rigorously assess one's own work as well as that of others.
- Design a statistical or operational research study to solve a real problem.
- Formulate statistical hypotheses and develop strategies to confirm or refute them.
- Interpret results, draw conclusions and write up technical reports in the field of statistics.
- Make efficient use of the literature and digital resources to obtain information.
- Select and apply the most suitable procedures for statistical modelling and analysis of complex data.
- Select statistical models or techniques for application in studies and real-world problems, and know the tools for validating them.
- Students must be capable of applying their knowledge to their work or vocation in a professional way and they should have building arguments and problem resolution skills within their area of study.
- Students must be capable of collecting and interpreting relevant data (usually within their area of study) in order to make statements that reflect social, scientific or ethical relevant issues.
- Students must be capable of communicating information, ideas, problems and solutions to both specialised and non-specialised audiences.
- Summarise and discover behaviour patterns in data exploration.
- Use quality criteria to critically assess the work done.

- Analyse data through inference techniques using statistical software.
- Analyse data using the generalised linear model.
- Analyse data using the model of linear regression.
- Analyse the residuals of a statistical model.
- Choose the relevant explanatory variables.
- Compare the degree of fit between several statistical models.
- Critically assess the work done on the basis of quality criteria.
- Detect and contemplate interactions between explanatory variables.
- Detect and respond to colinearity between explanatory variables.
- Draw conclusions about the applicability of models with the use and correct interpretation of indicators and graphs.
- Establish the experimental hypotheses of modelling.
- Identify response distributions with the analysis of residuals.
- Identify sources of bias in information gathering.
- Identify the response, explanatory and control variables.
- Identify the stages in problems of modelling.
- Identify the statistical assumptions associated with each advanced procedure.
- Make effective use of references and electronic resources to obtain information.
- Make slight modifications to existing software if required by the statistical model proposed.
- Measure the degree of fit of a statistical model.
- Predict responses, compare groups (causal value) and identify significant factors.
- Reappraise one's own ideas and those of others through rigorous, critical reflection.
- Students must be capable of applying their knowledge to their work or vocation in a professional way and they should have building arguments and problem resolution skills within their area of study.
- Students must be capable of collecting and interpreting relevant data (usually within their area of study) in order to make statements that reflect social, scientific or ethical relevant issues.
- Students must be capable of communicating information, ideas, problems and solutions to both specialised and non-specialised audiences.
- Summarise and interpret the results from classic and generalised linear models and from non-linear models on the basis of the objectives of the study.
- Use a range of statistical software to adjust and validate linear models and their generalisations.
- Use graphics to display the fit and applicability of the model.
- Use logistic regression to solve classification problems.
- Validate the models used through suitable inference techniques.

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/.