Biostatistics II

Code:

MSP12_14

Acronym:

MSP12_14

Keywords
Classification	Keyword
OFICIAL	Statistics

Instance: 2015/2016 - 2S

Active?	Yes
Responsible unit:	Clinical Epidemiology, Predictive Medicine and Public Health Department
Course/CS Responsible:	Master Degree Course in Public Health

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MSP	8	Master Degree Public Health - Official Plan12	1	-	3	27	81

Teaching language

Suitable for English-speaking students

Objectives

The objective of the course is to provide knowledge and comprehension on multivariable statistical models: (1) conceptual comprehension of the principles and practical applications of these models, (2) to be able to choose the appropriate analytical and model building strategy, discussing the different options for the inclusion of the variables, (3) understand and discuss the concepts of confounding and interaction, (4) to acquire practical knowledge how to realize an analysis and to interpret the results, using a statistical software.

The student will acquire skills to select the appropriate multivariate statistical model for the scientific question under analysis; to know how to build the model and to implement it an statistical software; to critical analyse and interpret the results.

Learning outcomes and competences

The syllabus of this unit responds to the stated objectives of understanding and being able to build multivariate models through building and discussion of diverse models, in particular by understanding the potentials and limitations of each multivariate approach.

Working method

Presencial

Program

Introduction to the analysis of multivariable models: Multiple Linear Regression, Analysis of Variance (fixed and random effects) and Logistic Regression. Introduction to non-parametric methods. Solution and discussion of application examples.

Mandatory literature

Armitage P & Berry G. ; Statistical Methods for Medical Research, Oxford: Blackwell Scientif Publications, 1987
Hosmer DW & Lemeshow S.; Applied Logistic Regression, , John Wiley and Sons., 1989
Siegel S. & Castellan N.J. Jr. ; Non-parametric statistics for Behavioral Sciences, McGraw-Hill, Inc. , 1988
Christensen, R. ; Analysis of variance, design and regression , Chapman and Hall, 1996
Altman DG.; Practical Statistics for Medical Research, Chapman and Hall, 1991
Collett D.; Modelling Binary Data, Chapman and Hall, 1996
Everitt BS; The Analysis of Contingency Tables, Chapman and Hall, 1977
Sokal, R.R. and F.J. Rohlf; Biometry - The Principles and Practice of Statistics in Biological Research, W.H. Freeman and Company, 1995

Teaching methods and learning activities

1. Self-direted learning


2. Expository, collaborative and active methodology


3. Applied learning (solving exercises)

 

A total of 81 hours of student’s work, of which 27 are of contact, distributed as follows is estimated:

14h of weekly theoretical sessions;

7h of weekly theoretical-practical sessions;

6h of laboratorial practice sessions.

Software

IBM SPSS Statistics

Evaluation Type

Evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	100,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	54,00
Frequência das aulas	27,00
Total:	81,00

Eligibility for exams

Presence at 75% of all lectures

Calculation formula of final grade

FC=FE