Applied statistics & data analysis, part 1
1. Aims and scope
The course will start with an overview of the "standard statistical toolbox", reviewing basic statistical approaches like correlation, linear regression and analysis of variance. Special emphasis will be put on test of assumptions and statistical model selection. This will naturally lead us to situations were standard assumptions are not fulfilled but the same type of questions is still to be answered.
The aim of the course is to introduce into basic statistical thinking and to enable you to look at your data statistically. Each block will be accompanied by practicals where example data are analyzed using the software package R.
2. Audience
This course is designed for IMPRS-gBGC PhD students, especially in the 1st and 2nd year. The course can be a 'stand-alone' (separate certificate) or a preparation for the >> part 2, Advanced statistics and data analysis.
3. Prerequisites
Basic knowledge of a language of scientific computing: R, Matlab (exercises will be in R)
4. Interested?
- Register here by October 05, 2013.
5. Instructor
6. Date
This part will take place on October 07-09, 2013 in Seminar room B0.002 @ MPI-BGC. Planned sessions:
- 09:00 - 10:30
- 10:45 - 12:15
- 13:15 - 14:45
- 15:00 - 16:30
7. What you need to prepare
Bring a laptop and make sure that a recent version of R is running on it.
You can download the most recent version here: http://www.r-project.org/.
You might like >> RStudio, an open-source integrated development environment that runs on all platforms. It nicely combines console, script editor, working directory, plots etc. into a an uncluttered layout that you can easily navigate. You need to have R installed before you can use RStudio as a development environment.
Please also make sure that you can access the internet via WLAN (BGC-users, if you have a BGC-account; BGC-guests, if you don't have an account)
8. Agenda
DAY 1: >> Mon, Oct. 07, 2013
Introduction to basic statistical tools:
- correlation,
- linear regression,
- analysis of variance,
- model selection
- video part 1 (1st morning session)
- video part 2 (2nd morning session)
- video part 3 (1st afternoon session)
- video part 4 (2nd afternoon session)
DAY 2: >> Tue, Oct. 08, 2013
What can be done if standard assumptions are not satisfied?
- dealing with variance heterogeneity,
- spatial and temporal autocorrelation
- video part 1 (1st morning session)
- video part 2 (2nd morning session)
- video part 3 (1st afternoon session)
- video part 4 (2nd afternoon session)
DAY 3: >> Wed, Oct. 09, 2013
Introduction into linear mixed models, basic ideas of nonparametric methods of curve estimation
9. Follow up.
>> Advanced Data analysis & statistics, part 2 (November 18-22, 2013).