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Code: | M4108 | Acronym: | M4108 |

Keywords | |
---|---|

Classification | Keyword |

OFICIAL | Mathematics |

Active? | Yes |

Responsible unit: | Department of Mathematics |

Course/CS Responsible: | Master's degree in Remote Sensing |

Acronym | No. of Students | Study Plan | Curricular Years | Credits UCN | Credits ECTS | Contact hours | Total Time |
---|---|---|---|---|---|---|---|

M:DR | 7 | The study plan from 2018 | 1 | - | 6 | 42 | 162 |

Teacher | Responsibility |
---|---|

Ana Rita Pires Gaio |

Theoretical and practical : | 2,00 |

Other: | 1,00 |

Type | Teacher | Classes | Hour |
---|---|---|---|

Theoretical and practical | Totals | 1 | 2,00 |

Ana Rita Pires Gaio | 0,00 | ||

Other | Totals | 1 | 1,00 |

Ana Rita Pires Gaio | 0,00 |

1. Train the student for regression analysis involving continuous or discrete responses (generalized linear models)

2. Implement statistical analyses in suitable software

3. Promote critical thinking in a data analysis process (data collection, modeling, interpretation of results, ...)

a) acquire knowledge about the organized collection of information

b) learn techniques and statistical models commonly used in data processing

c) know how to apply and implement the models studied in R

d) know how to correctly choose the learned statistical models for concrete problems

e) acquire a critical spirit and the ability to interpret the results obtained.

0.Brief review of basic statistical inference techniques - confidence intervals and hypothesis tests.

1- Introduction to the programming language in R software environment.

2. Pearson and Spearman correlation.

3. Simple linear regression.

4. Multiple linear regression. Model, parameter estimation, hypothesis tests for the coefficients, confidence intervals, prediction intervals, coefficient of determination, multicollinearity, model selection methods, model comparison, diagnosis.

5*. Analysis of variance: 1 and 2 factors.

6*. Generalized linear models. Logistic regression.

*Only one subject, from 5* or 6*, will be studied.

Julian Faraway; Linear Models with R, Taylor and Francis, 2009. ISBN: 1584884258

Julian Faraway; Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Chapman & Hall/CRC Texts in Statistical Science, 2006. ISBN: 158488424X

*Classes will be simultaneously theoretical and practical, with several examples of application and always making use of statistical programming. The used software will be the free programming language R.*

designation | Weight (%) |
---|---|

Teste | 37,50 |

Trabalho escrito | 25,00 |

Exame | 37,50 |

Total: |
100,00 |

designation | Time (hours) |
---|---|

Estudo autónomo | 110,00 |

Frequência das aulas | 42,00 |

Trabalho escrito | 10,00 |

Total: |
162,00 |

Attendency is not mandatory.

*1. The assignment consists of a written report and an oral presentation, and it is optional. 2. The grade obtained in the assignment cannot be improved.3. The evaluation in "época normal" (1st evaluation period) will include the marks from two tests (T1 and T2), each rated 10 points. Test T2 will be performed on the day the examination in "época normal" would take place.4. Evaluation in "época de recurso" (2nd evaluation period) will include a single exam, evaluating all subjects studied in the course. Marks from the tests (T1 and/or T2) will not be considered.5. *

2) The way the course will be provided is conditioned to the limitations imposed by FCUP according to the evolution of the COVID19 pandemic.

3) Th evaluation method is conditioned by the evolution of the COVID19 pandemic.

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Page created on: 2022-01-17 at 06:33:03

Page created on: 2022-01-17 at 06:33:03