IMPRS-gBGC course 'Applied statistics & data analysis' 2020, Basics

Category: Skill course
Credits: 0.2 per course day


1.  Basic statistics

1.1  Organizational issues

Date: September 7 - 9, 2020
Place: lecture hall @ MPI-BGC (depending on COVID-19 regulations)
Planned sessions:

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

Instructor: Dr. Jens Schuhmacher


1.2  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.


Learn R… Here is a list of useful online resources to help you bring your R skills to a new level.
The material from the R basics course might also be useful for you.

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.


1.3  Interested?

Prerequisites: Basic knowledge of a language of scientific computing: R, Matlab (exercises will be in R)
The course can be a 'stand-alone' or a preparation for the module 'Advanced statistics and data analysis'.


1.4  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:
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)


1.5  Preliminary agenda

Monday, September 7

Introduction to basic statistical tools

  • correlation
  • linear regression
  • analysis of variance
  • model selection
Tuesday, September 8

What can be done if standard assumptions are not satisfied?

  • dealing with variance heterogeneity,
  • spatial and temporal autocorrelation
Wednesday, September 9

Introduction into linear mixed models, basic ideas of nonparametric methods of curve estimation


2.  Feedback

Your feedback is valuable because it helps the instructors and organizers to improve the individual modules and the general structure of the workshop.
The survey results are available here. Statistics and statements should not be taken as an exhaustive or exclusive list.


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