Advanced Statistics

This course is primarily intended for Research Master students. There is only a limited number of spots available for PhD students

Description
In this course, you learn to tell a compelling story using data. You will both refresh and deepen your knowledge of statistics, learning about sophisticated statistical techniques such as multiple linear regression or survival analysis. You will be introduced to problem-solving practices in biostatistics and epidemiology using a data-driven approach. During the course, you will also master some valuable statistical software skills that will allow you to conduct your own data analysis.

Learning outcomes
The aim of the Advanced Statistics course is to deepen the statistical knowledge with regard to the design, implementation and evaluation of scientific research. At the end of the course, students are able to:

• Apply descriptive statistics for data exploration
• Perform a range of parametric and non-parametric statistical tests, such as the chi-square test, independent samples t-test, paired-test, Mann-Whitney U test, Wilcoxon signed rank test, and McNemar test
• Fit linear, logistic, and Cox regression models
• Utilize statistical inference techniques, including confidence intervals and hypothesis testing procedures, to draw conclusions from data and recognize situations in which such methods are appropriate.

Hours per week
10 weeks, 10-12 hours per week

Teaching methods
Lectures, practical session

Exam / assessment
Written assignment + exam.
At least 80% attendance is mandatory.