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Statistics for Life Sciences

Instructor: Sylvia Cremer

Teaching Assistant: Christopher Pull, Sina Metzler 

Description

This course addresses Experimental Design & Statistical Analysis across the Life Sciences. 

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. 

Requirements

Basic knowledge of R/attendance of introductory R course would be helpful, but not required. 

Credits

This course is worth 3 ECTS credits.    

Homework

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 

Final grade

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.

Schedule (subject to change)

Date Time Topic Location
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

Homework

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

Downloads

To take a look at the homeworks and additional downloads, please click here. (you must be logged in!)

Recommended Reading

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