Quantitative Methods

Code:

2GESS03

Acronym:

MQ

Keywords
Classification	Keyword
OFICIAL	Economics
OFICIAL	Management Studies

Instance: 2023/2024 - 1S

Active?	Yes
Responsible unit:	Agrupamento Científico de Economia
Course/CS Responsible:	Master in Health Care Economics and Management

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
GESS	24	Bologna Syllabus since 2012	1	-	7,5	56	202,5

Teaching language

Portuguese

Objectives

The course is designed for a one-semester in basic statistics and econometrics. The applications are mainly related to health. Students are expected to use specialized software to implement the different methods and techniques.

 

Learning outcomes and competences

Students are expected to learn the basic concepts in statistics and econometrics, and to be able to read and interpret elementary statistical and econometrics findings. Hands-on experience with specialized software will be acquired.

Working method

Presencial

Program

1. Brief review of descriptive statistics.

2. Random variables and probability distributions.

3. Statistical inference. Estimators. Hypothesis testing.

4. Introduction to Econometrics. The classical linear regression model. Inference in the linear regression model.

5. Some extensions of the linear regression model: dummy variables; choosing a functional form; heteroscedasticity.

Mandatory literature

Wayne W. Daniel; Biostatistics. A Foundation for Analysis in the Health Sciences, 10th Ed, Wiley Series in Probability and Statistics, 2014
M. Mendes de Oliveira, Luis Delfim Santos e Natércia Fortuna; Econometria, 2ª ed., Escolar Editora, 2018

Complementary Bibliography

Andrew M. Jones, Nigel Rice, Teresa Bago d'Uva and Silvia Balia; Applied Health Economics, 2nd ed, Routledge, 2013
Bento Murteira, Carlos Silva Ribeiro, João Andrade e Silva, Carlos Pimenta; Introdução à Estatística, Escolar Editora, 2010. ISBN: 9789725922828
Bland, Martin ; An introduction to medical statistics, 4th ed, Oxford University Press, 2015
Fernanda Figueiredo, Adelaide Figueiredo, Alexandra Ramos e Paulo Teles; Inferência Estatística, Escolar Editora, 2017
Griffiths, W.E., Hill, R. C. and Lim; Using EViews for Principles of Econometrics 4th ed., John Wiley & Sons, 2011
Hoffman, Julien I. E.; Biostatistics for medical and biomedical practitioners , Elsevier, 2015
Rao, P. ; Statistical Methodologies with Medical Applications , John Wiley & Sons, 2017
Stewart, A. ; Basic Statistics and Epidemiology: A Practical Guide, 4th ed., Taylor & Francis Group, 2016
Vach, W.; Regression Models as a Tool in Medical Research , Taylor & Francis Group, 2013

Teaching methods and learning activities

Classes will be held in computer labs, allowing students’ access to hands-on experience with computer applications using specialized software (EViews).  

 

Software

EViews

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Teste	50,00
Trabalho escrito	50,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	118,50
Frequência das aulas	42,00
Trabalho escrito	42,00
Total:	202,50

Eligibility for exams

Regular attendance is expected, but all students may take the final exam.

Calculation formula of final grade

Two possibilities of assessment: final exam or distributed assessment. 

In the case of the distributed assessment, students will have to carry out a group work during the semester and a comprehensive test (the use of computers is required) on the date of the normal period exam. The final grade is the weighted average of both components, with equal weights. A positive grade requires a weighted average of 9,5 (out of 20) and no partial score below 7,0.

In the case of the final exam, the use of computers is required and the final classification corresponds to the grade obtained in the exam.