Objectives and competences

Pedagogical Objectives & Outcomes

This program trains specialists in Probability and Statistics for high-quality research. It focuses on scientific methodology, covering research design, study types, and results interpretation. Students will develop skills in academic writing and the application of statistical tools using specialized software.

Expected Outcomes:

• Master research methodology stages and formalize random experiments.
• Apply and assess parametric, non-parametric, and goodness-of-fit tests.
• Plan and conduct studies following experimental design (DOE) principles.
• Analyze complex data and interpret results in a research context.
• Prepare scientific reports, articles, and presentations.
• Critically evaluate third-party statistical analyses.
• Build a foundational reasoning for autonomous learning of advanced methods.

Teaching Methodologies

The Applied Statistics course focuses on reflection and discussion related to research and to the collection, treatment, analysis, and interpretation of data. It is organized into weekly theoretical classes of 1.5 hours, dedicated to introducing and summarizing the main concepts, and weekly theoretical practical classes of 1.5 hours, where students apply statistical methods through practical work and the use of software tools such as Excel and SPSS. These sessions promote the analysis, interpretation, and discussion of the results obtained. Throughout the semester, students are also required to complete an individual or small group project involving research, analysis, and the use of supporting materials, including scientific texts and relevant technical documents.

Syllabus

Course Syllabus

Part 1: Research Methodology & Design
Topic selection; variable identification; population and sampling; Factorial Design; data collection planning; questionnaire design; results interpretation and reporting.

Part 2: Foundations of Biostatistics
Sampling; variable classification; biological variability; independent vs. dependent observations; prevalence vs. incidence; principles of estimation and hypothesis testing.

Part 3: Exploratory Data Analysis (EDA)
Descriptive statistics and data visualization using SPSS software.

Part 4: Parametric Tests
T-tests (independent/paired); ANOVA (one-way, multiple comparisons, and repeated measures).

Part 5: Non-Parametric Tests & Categorical Data
Contingency tables; independence and homogeneity tests; tests for independent/paired samples; inference on proportions, relative risk, and odds ratios.

Part 6: Correlation & Regression
Linear and non-linear regression models.
Practical components involve real-world case studies in all modules.