Instructor: Sylvia Cremer
Teaching Assistant: Christopher Pull, Sina Metzler
First, we will consider how to plan experiments with the statistics in mind and the types of data that are commonly used in the life sciences. Next, we will discuss how to visually explore data and effectively present information through graphs. In the rest of the lectures, we will focus on the different statistical approaches used in the life sciences, in particular, comparing frequencies and means (between two or more groups), performing correlation and regression analysis, and the powerful and versatile general linear model. We will also introduce survival statistics and multivariate analysis tools.
Lectures will focus on the background and application of the statistical methods. Recitations will give a hands-on introduction to the analyses using the statistical programme R. Homework will train you on the content covered in the lectures.
Basic knowledge of R/attendance of introductory R course would be helpful, but not required.
This course is worth 3 ECTS credits.
In total there will be 4 homework exercises. These will be handed out at the end of the recitations and must be handed back in to the TAs at the following week's recitation.
Homework can be done alone or in pairs. If working in a pair, please hand in a single homework with the names of both students.
For the homework, the grading scale works as follows:
≥ 85% = 1, 70-84% = 2, 60-69% = 3, 50-59% = 4, ≤ 49% = fail
To pass the course, it is required to:
a) Score at least 50% in all homeworks.
b) Gain at least a 4 in the oral presentations at the end of the course; details of the presentation topic are handed out in the first week of class after the Christmas break and presentations will be held on Monday 23.1. and Wednesday, 25.1.2017.
The final grade comprises both the grades obtained from the homeworks and the oral presentations, which are weighted equally.
|Mon, 28-Nov-2016||08:45 - 10:00 & 11:45 - 12:30||Lecture 1: Introduction (Statistical Testing & Experimental Design)||Mondi 3|
|Wed, 30-Nov-2016||08:45 - 10:00||Lecture 2: Data Types & Distributions||Mondi 3|
|Mon, 05-Dec-2016||08:45 - 10:00||Lecture 3: Graphs||Mondi 3|
|Mon, 05-Dec-2016||11:45 - 12:30||Recitation 1 (Lecture 2 & 3)||Mondi 3|
|Wed, 07-Dec-2016||08:45 - 10:00||Lecture 4: Correlation & Regression||Mondi 3|
|Mon, 12-Dec-2016||08:45 - 10:00||Lecture 5: Frequency Tests||Mondi 3|
|Mon, 12-Dec-2016||11:45 - 12:30||Recitation 2 (Lecture 4 & 5); Submission Homework 1||Mondi 3|
|Wed, 14-Dec-2016||08:45 - 10:00||Lecture 6: Comparison of means (2 groups)||Mondi 3|
|Mon, 19-Dec-2016||08:45 - 10:00||Lecture 6: Comparison of means (>2 groups)||Mondi 3|
|Mon, 19-Dec-2016||11:45 - 12:30||Recitation 3 (Lecture 6 & 7); Submission Homework 2||Mondi 3|
|Wed, 21-Dec-2016||08:45 - 10:00||Lecture 8: Survival Analysis||Mondi 3|
|Mon, 09-Jan-2017||08:45 - 10:00 & 11:45 - 12:30||Lecture 9: General(ised) Linear Models||Mondi 3|
|Wed, 11-Jan-2017||08:45 - 10:00||Recitation 4 (Lecture 8 & 9); Submission Homework 3||Mondi 3|
|Mon, 16-Jan-2017||08:45 - 10:00||Lecture 10: Multivariate Data Analysis||Mondi 3|
|Mon, 16-Jan-2017||11:45 - 12:30||Recitation 5 (Lecture 10); Submission Homework 4||Mondi 3|
|Wed, 18-Jan-2017||08:45 - 10:00||Lecture 11: Summary||Mondi 3|
|Mon, 23-Jan-2017||08:45 - 10:00 & 11:45 - 12:30||Student Oral Presentations||Mondi 3|
|Wed, 25-Jan-2017||08:45 - 10:00||Student Oral Presentations||Mondi 3|
|File||Date set||Due Date|
|Homework 1 (Data Types, Distributions & Graphs)||Mon, 05-Dec-2016||Mon, 12-Dec-2016|
|Homework 2 (Correlation, Regression & Frequency Tests)||Mon, 12-Dec-2016||Mon, 19-Dec-2016|
|Homework 3 (Comparison of means)||Mon, 19-Dec-2016||Mon, 09-Jan-2017|
|Homework 4 (General(ised) Linear Models)||Mon, 09-Jan-2017||Mon, 16-Jan-2017|
|Exam||Mon, 09-Jan-2017||Mon, 23-Jan-2017/Wed, 25-Jan-2017|
To take a look at the homeworks and additional downloads, please click here. (you must be logged in!)
The following books will be helpful in completing the course and performing statistics in general:
1) Good introduction to statistics and their application in R: Peter Dalgaard, Introductory Statistics with R
2) Good introduction to statistics in general : Steve McKillup, Statistics Explained
2) Excellent reference book on all aspects of statsitics and R: Andy Field et al., Discovering Statistics in R