Intended learning outcomes (knowledge, skills and competences to be developed by the students):
Development of the autonomous ability to use statistics, as well as data analysis tools, with a particular focus on the biomedical context.
It is intended, with this Curricular Unite, to ensure the independence of students in the field of scientific research, in terms of: i) formulation of hypotheses and scientific questions in a statistically coherent and consistent way; (ii) design of the studies necessary to obtain the respective answers; iii) execution of these studies, knowing how to treat and analyze the data at progressively deeper levels; iv) understanding of the limitations of the tools used, having the critical ability to analyze data from various perspectives; v) ability to analyze results and draw statistically relevant and valid conclusions; and vi) knowledge of scientific writing of statistical methods and results, as well as their understanding and analysis.
Syllabus:
Module 1: Descriptive statistics and exploratory data analysis
- Qualitative analysis and scientific thinking;
- Variables and scientific questions;
- Sampling;
- Data visualization;
- Basic distributions;
- Parameters and estimation;
- Exploratory data analysis;
- Introduction to SPSS.
Module 2: Inferential Statistics I
- Effects and errors;
- Confidence intervals;
- Hypothesis tests (mean, proportion and dependence);
- Normality tests;
- Parametric vs. non-parametric tests;
- Risk analysis;
- Diagnostic tests and ROC curves;
- Application in SPSS.
Module 3: Inferential Statistics II
- Dependence and covariance;
- Linear regression;
- Logistic regression;
- Survival analysis (Kaplan-Meier and Cox);
- ANOVA;
- ANCOVA;
- GLM;
- Application in SPSS.
Module 4: Data analysis
- Data processing;
- Reduction of parametric space;
- Distance measurements;
- Data classification - supervised and unsupervised;
- Bayesian algorithms;
- Clinical applications;
- Introduction to R.