Objectives and competences

Objectives
Transfer basic knowledge of statistics and some analysis methods for students to acquire basic skills of organization, representation, inference, and interpretation of data/information.
Promote the interest in statistics, transferring knowledge to help them to be critical users of that knowledge, with autonomy to extract information from data and to understand the situations they represent.
Demonstrate the essential role of statistics in the definition and understanding of the fundamental issues of health/nutrition/microbiology.
Create solid skills and extended statistical-based domain in the students, for research in the areas of health/nutrition/microbiology.
Prepare the students for more advanced topics of analysis and interpretation of data, which will be an integral part of their professional activities in this area of knowledge.
Foster scientific and critical thinking, essential in making decisions.

Results
Appropriate use of statistics in the areas of health/nutrition/microbiology.
Understanding the statistical reasoning used in scientific articles in the area.
Master knowledge related to data analysis and statistical inference methodologies.
Interpretation of results from basic and advanced statistical procedures.
Use statistical programs for representation, interpretation and data analysis.

Teaching Methodologies

The course is structured in lectures (19.5 h), theoretical-practical (33 h), and tutorial guidance (6h) classes. Theoretical contents with examples of application are presented in lectures. In theoretical-practical classes, the students solve exercises that are gathered in worksheets covering the whole topics of the program, to consolidate and apply the acquired knowledge.

In Biostatistics students learn to use the IBM SPSS Statistics program, and all exercises are solved in this software. Tutorial guidance classes are intended to support, clarify questions, and guide students in using SPSS.

Syllabus

Lectures

1. Basic concepts: population/sample, random variables, measurement scales, and sampling
2. Descriptive statistics: qualitative and quantitative data, measures, graphical representations
3. Probability distributions of continuous random variables: the normal distribution, standard normal, Student t, chi-square and F
4. Random sampling and sampling distributions
5. Interval estimation
6. Hypothesis testing: procedures, critical regions, significance level, p-value
7. Association between variables: relative risk and odds ratio, Qui-Square test for independency, correlation coefficients
8. Linear and multiple linear regression
9. Parametric tests: single mean, the difference between two means, variance, the ratio of two variances, proportion and difference between two proportions, dependent and independent data, analysis of variance
10. Non-parametric tests: normality of data and homogeneity of variance, dependent and independent data, one- and multi-sample tests

Theoretical-practicals
Solving exercises on all topics listed in the Syllabus.
Use of software (IBM SPSS Statistics) as a tool for data analysis.