2023
Biostatistics
Name: Biostatistics
Code: MAT12718D
6 ECTS
Duration: 15 weeks/156 hours
Scientific Area:
Mathematics
Teaching languages: Portuguese
Languages of tutoring support: Portuguese, English
Regime de Frequência: Presencial
Presentation
To provide several statistical models used in the design and analysis of experiences, their assumptions and critical interpretation and also some multivariate statistics methods.
Sustainable Development Goals
Learning Goals
At the end of this course students should have acquired knowledge, skills and competences:
- To understand some fundamental principles and concepts of experimental designs.
- To understand statistical methods in papers related with their research field.
- To apply some essential statistical tools to analyse data in their research area.
- To design an experiment and to have the capacity to select and organize information.
- To select the appropriate statistical model and to know how to validate the model.
- To validate the assumptions of different statistical approaches.
- To recognize and to know how and when to apply nonparametric approaches.
- To learn autonomously and to be able to adapt to new situations.
- To be able to validate and interpret, in a critical way, the results obtained with the statistical software R.
- To understand some fundamental principles and concepts of experimental designs.
- To understand statistical methods in papers related with their research field.
- To apply some essential statistical tools to analyse data in their research area.
- To design an experiment and to have the capacity to select and organize information.
- To select the appropriate statistical model and to know how to validate the model.
- To validate the assumptions of different statistical approaches.
- To recognize and to know how and when to apply nonparametric approaches.
- To learn autonomously and to be able to adapt to new situations.
- To be able to validate and interpret, in a critical way, the results obtained with the statistical software R.
Contents
Design of experiments (sampling, statistical and biological assumptions)
Analysis of variance models (crossed, nested and repeated measures)
Linear and nonlinear regression models. Correlation. Association.
Analysis of Covariance.
Non-parametric methods.
Introduction to statistical process control.
Multivariate statistical methods (principal components analysis, factor analysis and clusters).
Analysis of variance models (crossed, nested and repeated measures)
Linear and nonlinear regression models. Correlation. Association.
Analysis of Covariance.
Non-parametric methods.
Introduction to statistical process control.
Multivariate statistical methods (principal components analysis, factor analysis and clusters).
Teaching Methods
Theoretical-practical lessons mainly lectured with a blackboard, with e-learning tools, and slide shows.
Laboratorial practical lessons using R statistical software, with step-by-step critical interpretation of the
outputs and with a summary of the main conclusions. Motivation of students attendance to the classroom
and students continuous work. Introduction to theoretical concepts appealing to direct applications in
food science to illustrate the importance of course contents. Exercises with emphasis in the resolution of
real and current problems, looking to develop the interest for the course and to show its utility.
Laboratorial practical lessons using R statistical software, with step-by-step critical interpretation of the
outputs and with a summary of the main conclusions. Motivation of students attendance to the classroom
and students continuous work. Introduction to theoretical concepts appealing to direct applications in
food science to illustrate the importance of course contents. Exercises with emphasis in the resolution of
real and current problems, looking to develop the interest for the course and to show its utility.
Teaching Staff
- Paulo de Jesus Infante dos Santos [responsible]
Certificações
