Advanced Methods in Data Analysis

Method 1-The curricular unit will be taught in a theoretical-practical format, in an active expository manner, incorporating the conceptual elements of univariate, bivariate, and multivariate statistical science.
Method 2 – Apply the methods learnt with a practical component by integrating simulation models applied to different realities of study in Health, Nutrition and Metabolism

The knowledge taught will be assessed through an individual evaluation (written test) in which the various methodologies acquired in data analysis using specialised statistical software will be presented.

The master’s student will acquire specialised knowledge about analytical models in Statistics at the level of data analysis, with application in the core areas of research: Food, Nutrition, and Metabolism.
In terms of skills, the master’s student will acquire the ability to plan research strategies and analytical data collection (qualitative and quantitative) in accordance with best practices in the research process and, as a researcher, to make decisions on the analytical choice of robust models for analysing and processing data.
The master’s student should also acquire skills in the use of specialised data processing software, the ability to analyse data resulting from statistical modelling, and the distinction and choice of simple or multifactorial statistical research methodologies

1 – Fundamental concepts in basic statistics based on descriptive and inferential analysis models (univariate and bivariate) concerning hypothesis testing in probability.
2 – As far as advanced statistical methods are concerned, models will be used that attempt to carry out multivariate exploratory analysis and predictive models.
3 – Generalised Linear Models – Factorial Analysis of Variance and Multivariate Analysis of Variance.
4 – Univariate, Multivariate and Hierarchical Linear Regression Models.
5 – Univariate and Multivariate Logistic Regression Models.
6 – Exploratory Cluster Analysis Models.
7 – Factor Analysis Models.
8 – Meta-analysis and meta-regression.
9 – Application of specialised data analysis software.

NAO