IMPRS course 'Applied statistics & data analysis - Basics'

1.  General information

Date: September 25-27, 2023

Planned sessions:

• 09:00 - 10:30
• 10:45 - 12:15
• 13:15 - 14:45
• 15:00 - 16:30
  

Location: MPI-BGC, B0.002
Teacher: Jens Schumacher
Category: Skill course
0.6 CP for the whole course

2. 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.

The course can be a 'stand-alone' or a preparation for the module 'Advanced statistics and data analysis'.

Prerequisites:

Basic knowledge of a language of scientific computing: R, Matlab (exercises will be in R).

The (material from the) R course - Basics might be useful for you. Here is a list of useful online resources to help you bring your R skills to a new level.

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; eduroam or BGC-guests, if you don't have an account).

3.  Preliminary agenda

Monday, September 25:
Introduction to basic statistical

• correlation
• linear regression
• analysis of variance
• model selection

Tuesday, September 26:
What can be done if standard assumptions are not satisfied?

• dealing with variance heterogeneity,
• spatial and temporal autocorrelation

Wednesday, September 27:
Introduction into linear mixed models, basic ideas of nonparametric methods of curve estimation

4.  Registration

Please register here.