Data Analysis

Recommended Prerequisites

Not applicable.

Teaching Methods

The methodology includes lectures using audiovisual aids and practical classes in the computer room. The aim is to promote active and participatory learning, focusing on interaction to understand the logic behind statistical tests better. Real problems are proposed, and students are challenged to solve them using statistical methodologies in peer collaboration or discussion with the teacher.

Learning Outcomes

This course aims to make students aware of the importance of data processing and analysis, with particular emphasis on health research and health organizations. The aim is to familiarize students with simple statistical methodologies, namely descriptive methods, hypothesis tests for studying comparisons and associations between variables, and regression models. An additional aim of the unit is to introduce the R software for statistical analysis. At the end of the course, students should be able to develop data analysis processes, either independently or in working groups.

Work Placement(s)

No

Syllabus

-- Introduction to statistics - Basic concepts.

- Univariate descriptive statistics and graphical representation of data,

- The normal distribution and sampling distributions: Concepts

- Correlation and independence - Linear correlation.

- Inferential statistics

* Estimation and hypothesis testing

* Independence test and chi-squared test.

- The linear regression model

* Presentation of the model

* Hypotheses underlying the model

* Measures of the overall fit of the model

* Issues and aspects of specifying the linear model

* Hypothesis testing

* Interpretation of parameters

Head Lecturer(s)

Óscar Manuel Domingos Lourenço

Assessment Methods

Assessment
In-class presentation: 20.0%
Exam: 40.0%
Project: 40.0%

Bibliography

* Wooldridge, Jeffrey M., Introductory Econometrics : a Modern Approach. Mason, Ohio :South-Western Cengage Learning, 2019.

* McClave, J. T., Benson, P. G., & Sincich, T. (2022). Statistics for business and economics. Pearson Education.

* Daniel, Wayne W., and Chad L. Cross. Biostatistics: a foundation for analysis in the health sciences. Wiley, 2019.

* Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for data science. O'Reilly Media, Inc.

* Wilson, J., Chen, D. G., & Peace, K. E. (2023). Statistical analytics for health data science with SAS and R. Chapman and Hall/CRC.