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Research Group Leader

Prof. Dr. Matthias Bethge
Prof. Dr. Matthias Bethge
Phone: +49 7071 29-89017
Fax: +49 7071 29-25015
mbethge[at]tuebingen.mpg.de

 

Secretary: Heike König
Phone: +49 7071 29-89018
Fax: +49 7071 29-25015
heike.koenig[at]tuebingen.mpg.de
 

 

Introduction to statistics (Winter term 2010/2011)

Course Content
The application of statistical techniques is ubiquitous in quantitative neuroscience research. Knowledge of the basics of statistic is therefore essential for the correct understanding and interpretation of results published in the literature. Also, publishing and properly reporting the results of one's own research critically depend on statistical skills. This lecture will cover basic concepts important to experimentally working molecular neuroscientists, including descriptive statistics, hypothesis testing, and correlation and regression analysis.

The aim of the lecture is to provide a toolkit for basic statistical analysis and an understanding of important statistical concepts. At the end, students are able to apply the basic techniques to data and acquire additional knowledge on their own. They will also be able to critically assess the statistical techniques used in publications and be aware of the most common pitfalls in the use of statistics.


The course consists of lectures, exercises at home, computer exercises in class and a small project, where a statistical problem must be solved independently and presented afterwards.

Location and Time
Wednesday, 9:30 am - 11.15 am, Lecture Hall 2, Graduate School
Important Information
Download information sheet for details about times, exercises and grading.

On days with computer exercises, the course will be 9.30-12.00.


For further information, visit the course website at the Graduate School of Cellular & Molecular Neuroscience.

Slides
 
Lecture 1: Introduction & Descriptive Statistics

Lecture 2: Probability theory & Normal distribution

Lecture 3: Error bars & T-test

Lecture 4: Two-sample t-test, Mann-Whitney test, Paired t-test

Lecture 5: Regression and Correlation

Lecture 6: Analysis of Variance

Lecture 7: Experimental design and two-factor ANOVA

 
Exercises
 
Date due

Assignment

Additional Downloads
20.10.

27.10.

 
3.11.


17.11.


24.11.


8.12.

15.12.

22.12.

 
Extra material
 
Matlab exercise 1: instructions

Matlab exercise 1: xls

Matlab exercise 1: mat

Samuels/Witmer: Confidence intervals for proportions

Matlab exercise 2: instructions

Matlab exercise 2: mat

Matlab exercise 3: regression

Matlab exercise 3: data

Matlab exercise 3: ANOVA

Matlab exercise 3: regression

 
Lecturer
Philipp Berens and Alexander Ecker
Last updated: Wednesday, 27.02.2013