Biostatistics

Code:	LBBA1153		Acronym:	BIOEST
Scientific Area:	Statistics

Occurrence: 2022/23 - 2S

Teaching Area:

Ciências Matemáticas - CM

Courses

Acronym	Nº de Estudantes	Plano de Estudos	Academic Year	Credits	Horas Contacto	Total Hours
LBBA	21	Despacho n.º 7512/2022 de 15/06	1º	5	60	140

Hours Actually Taught

LBBA_1

Ensino Teórico:	5,00
Theoretical and Practical:	0,00

Teaching - Weekly Hours Ensino Teórico: 1,00 Theoretical and Practical: 3,00 Type Teacher Classes Hours Ensino Teórico Totals 1 1,00 Theoretical and Practical Totals 1 3,00 Rita Maria de Almeida Neres - ESA   3,00

Teaching - Responsabilities Teacher Responsabilidade Ana Maria Ambrósio Paulo - ESA Responsável

Learning outcomes of the curricular unit (knowledge, skills and competences to be developed by the students)

Acquisition of the basic concepts of biostatistics: descriptive measures, probabilities, selected probability distributions, sample and population, representativeness of the sample. These knowledge are essential requirements for sampling, data description and interpretation, curve fitting, statistical inference by confidence intervals and hypothesis testing.

Syllabus

Introduction to Statistics. Descriptive statistics. Curve fitting (linear and exponential relationships, method of the least squares). Probabilities and probability laws. Discrete and continuous random variables: populations and probability distributions, mean and variance. Contingency tables. Selected discrete and continuous distributions. Samples and sampling distributions: central limit theorem, sampling distribution of xbar, confidence intervals. Hypothesis testing for the mean (one-sample, two samples).

Demonstration of the syllabus coherence with the curricular unit's learning objectives

The syllabus is devoted to the upgrade of theory of probabilities and desciptive statistics, and introduces probability distributions and the basis of statistical inference. This will allow the students to apply descriptive methods to summarize data and to apply the parametric one and two sample inference methods.

Teaching methodologies (including evaluation)

Explanatory matter in lecture sessions and application in practical classes.
Evaluation through 2 tests (T1 and T2) that can provide the exam exemption when T1 and T2 individual marks are greater or equal to 8 (8/20) and (T1+T2)/2 is greater or equal to 10 (10/20). The inscription on the first test is mandatory.
The final exam consists of a written test.

Demonstration of the coherence between the teaching methodologies and the learning outcomes

The presentation of the theoretical concepts will take place in lecturing classes and its application with practical examples in practical sessions. The extra-work beyond class hours, with tutorial support will lead to the accomplishment of the proposed objectives.

Bibliography (Mandatory resources)

Eason G.; Coles C. W.; Gettinby G., 1986. Mathematics and Statistics for the Bio-Sciences. J Wiley&Sons.
Martins, E. G., 2001. Noções Básicas sobre Amostragem - Introdução à Inferência Estatística, DEIO, FCiências da Universidade de Lisboa.
Montgomery D.C., 2005. Design and Analysis of Experiments. John Wiley&Sons Inc.
Murteira B., 1999. Análise Exploratória de Dados - Estatística Descritiva. Mc Graw-Hill de Portugal.
Murteira B., Ribeiro C.S., Silva J.A., Pimenta F., Pimenta C., 2015, Introdução à Estatística (3ª ed.) Escolar Editora.
Reis E., Melo P., Andrade R., Calapez, T. Estatística Aplicada, vols 1 e 2. Sílabo, 2015.
Taylor, J. R., 1997. An Introduction to Error Analysis. Oxford University Press.