Crash Course in Statistics for Neuroscience Center Zurich
An annual course for students of the ZNZ to get hands-on acquaintance with statistics and its application to research. The goal of this course is to establish basic concepts required for good statistical practice and to give insights into the statistical programming environment R using RStudio and reporting tools such as Rmarkdown. Using real world examples we will show possibilities, limitations, and caveats of using statistics in neurosciences and research in general. This course gives 2 ECTS credit points. If you miss more than 3h there will be no credits awarded.
Date + Time
July 8 - 12, 2018, 09:00 - 16:00 (CEST) at University Campus Irchel, room will be announced soon.
Registration + Administration
Heidi Gauss, hgauss@neuroscience.uzh.ch, Tel. 044 635 33 82
Aims of the course
Participants will come to…
- have a basic knowledge of statistics
- gain familiarity with R and Rstudio, as well as R packages from CRAN
- develop a sense for good data visualization
- use R to produce customized plots
- scrutinize statistical results in publications
- use R to perform – and interpret! – commonly used statistical hypothesis tests
- fit linear models/ANOVAs to suitable data, check model assumptions and interpret the respective R output
The way I teach
- Each day of the course will cover a certain topic of statistics and its application in research.
- The day will start with a two hour theoretical session, followed by a case study of a neuroscience publication, where we see how the concepts are (ab)used in science. In the afternoon the concepts are motivated using R, where the participants will work among themselves. - Please bring your own laptop and ensure that you have an internet connection at the venue, power supply is available.
- Install R & RStudio - there are many guides on the internet on how to do it.
- A script containing documentation, comments and exercises, as well as the slides of the theoretical parts in the mornings, will be made available for download at the beginning of the course.
- The lecturer will be coaching the participants on the tasks they will solve such that everyone can work with their own speed of learning to use R.
Course material
All course material will be available online during the course. Make sure your internet connection works.
Schedule
| Time |
Mon |
Tue |
Wed |
Thu |
Fri |
| 09:15 - 11:00 |
Why? |
Random variables |
Visualisation |
Tests |
Caveats |
| 11:15 - 12:00 |
Case 1 |
Case 2 |
Case 3 |
Case 4 |
Case 5 |
| 13:00 - 16:00 |
R? |
Handling data |
Plotting |
Modelling |
What next? |
Literature
The following list of books is not required for this course, but I strongly recommend their lecture anyway.
Questions
For questions regarding the topic and teaching of the course contact Daniel Stekhoven, Tel. 044 632 21 61.
Questions regarding registration and administration of the course contact Heidi Gauss, Tel. 044 635 33 82.