The objectives of the course include the knowledge of statistical models and estimation techniques and their adequate application to real situations, together with the development of programming and analitical skills with the Stata software.
1. Basics of Stata
2. Data management – basics
3. Descriptive statistics
4. Multiple linear regression for cross-sections
5. Linear models for longitudinal data
6. Logistic regression
7. Survival models
The students finishing the course must have learned:
1) Stata basics; data management skills in Stata;
2) how to use descriptive statistical methods to extract the relevant information from a data set;
3) the fundamental assumptions of the linear regression model for cross-section and panel data;
4) how to develop a linear regression statistical analysis with Stata;
5) the fundamental assumptions of logistic regression and its implementation with Stata;
6) how to characterize time-to-event data, and estimation of survival models in Stata.
PhD students at Universidade NOVA de Lisboa and PhD holders working at NOVA (Researchers, Postdocs and Professors) .
STUDY LOAD AND EVALUATION
12 h (2 days)
Lectures and practical activities - 12 h
Classes comprise lectures and exercises. Therefore, theoretical models are considered together with examples, exercises and empirical analysis of real data with Stata Software.
There is no formal evaluation at the end of the course; however, students are encouraged to solve practical cases of data analysis, exploring the statistical techniques covered each day, using the Stata Software. Results of the students work will be presented in the practical sessions.
Juul, S & Frydenberg, M. (2014). An Introduction to Stata for Health Researchers, 4th edition. Stata Press