Applied Statistics I

Cod: 21041

Department: DCET

Department: DCET

ECTS: 6

Scientific area: Mathematics

Scientific area: Mathematics

Total working hours: 156

Total contact time: 26

Total contact time: 26

This course is the consolidation of knowledge in the area of parametric and nonparametric Statistical Inference. It also presents a small computational component in R. It is also intended, with the introduction of one-way analysis of variance, initiating the study of advanced techniques for the comparison of levels.

Statistical inference

Hypothesis Tests

Confidence Intervals

Analysis of Variance

Hypothesis Tests

Confidence Intervals

Analysis of Variance

By completing this course the student should be able to interpret and solve problems of statistical inference, Parametric and Non Parametric. Given a practical situation the student should be able to identify whether the data comes from a population with a given distribution and whether independence between samples is verified. The student must know how to establish hypothesis testing and confidence intervals, and to interpret the results. The student will be able to use R to perform the most common hypothesis test and estimation techniques. Students also acquire skills that allow knowledge to use statistical techniques to compare various levels of a factor.

1. Introduction to Statistical Inference

2. Point estimation

3. Estimation by Confidence Intervals

4. Parametric Hypothesis Tests

5. Non Parametric Hypothesis Tests

6. Analysis of variance with one factor

R will be introduced as a computational tool for data analysis and interpretation in 2, 3 and 4.

Compulsory Reading:

Figueiredo, F., Teles, P., Figueiredo, A., Ramos A., Inferência Estatística Problemas resolvidos e propostos com aplicações em R, Escolar Editora (2017)

Complementary Reading (Optional):

T. Oliveira & A. Oliveira: Estatística Computacional (Available online in the classroom)

T. Oliveira: Estatística Aplicada, cap.1-5. Edições Universidade Aberta, 2004. (Available online in the classroom)

Elizabeth Reis, Paulo Melo, Rosa Andrade, Teresa Calapez, Estatística Aplicada – Vol 2. Edições Sílabo, 2018.

Pedrosa, A. C., Gama, Sílvio Marques A., Introdução Computacional à Probabilidade e Estatística, Porto Editora, 2007.

Figueiredo, F., Teles, P., Figueiredo, A., Ramos A., Inferência Estatística Problemas resolvidos e propostos com aplicações em R, Escolar Editora (2017)

Complementary Reading (Optional):

T. Oliveira & A. Oliveira: Estatística Computacional (Available online in the classroom)

T. Oliveira: Estatística Aplicada, cap.1-5. Edições Universidade Aberta, 2004. (Available online in the classroom)

Elizabeth Reis, Paulo Melo, Rosa Andrade, Teresa Calapez, Estatística Aplicada – Vol 2. Edições Sílabo, 2018.

Pedrosa, A. C., Gama, Sílvio Marques A., Introdução Computacional à Probabilidade e Estatística, Porto Editora, 2007.

E-learning

Continuous assessment is privileged: 2 or 3 digital written documents (e-folios) during the semester (40%) and a
presence-based final exam (p-folio) in the end of the semester (60%). In due time, students can alternatively choose to perform one
final presence-based exam (100%).

Pre-requisites: knowledge of basic probabilities and statistics (UC 21037 Elements of Probabilities and Statistics)