About this course
In general, the course aims to reach the following end terms: Knowledge: knowledge on basis statistical data analysis techniques Skills: the student will be able to translate a research question into a statistical problem, which he/she can solve using basic statistical methods. In particular, these methods are related to the analysis of means (e.g. t-tests, ANOVA) and regression analysis.
The student will be capable of performing the data analysis, and of interpreting the results, and he/she will be able to translate these conclusions back to the context of the original research question.
- descriptive statistics (means, medians, percentiles, …);
- some common distributions: normal,
- binomial, multinomial;
- basics of statistical inference:
- confidence intervals and statistical hypothesis tests;
- statistical tests for association in
- contingency tables;
- comparison of 2 means (t-test and mann-whitney test);
- comparison of k means (f-test and
- kruskal-wallis test);
- multiple comparison of means (tukey, bonferroni,..);
- 2-way anova and interaction;
- multiple way anova;
- simple and multiple regression analysis.
After successful completion of this course students are expected to be able to:
- understand the basics of statistical data exploration and statistical inference;
- perform basic statistical data analyses using the software R;
- recognise important problems in the study design/analyses and knows how these may affect the conclusions from the statistical data analysis;
- correctly report the results of a statistical data analysis in a scientific report.
A basic knowledge of calculus and probability theory (random variables, probability and distributions) is required.
- CreditsECTS 5
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