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Thu 21 Jun - Fri 22 Jun 2018
09:30 - 17:00

Venue: Bioinformatics Training Room, Craik-Marshall Building

Provided by: Bioinformatics


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Statistics for Biologists in R

Thu 21 Jun - Fri 22 Jun 2018


This course is intended to provide a strong foundation in practical statistics and data analysis using the R software environment. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences using the R software package.

In this course we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to generalised linear model analysis. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory.

After the course you should feel confident to be able to select and implement common statistical techniques using R and moreover know when, and when not, to apply these techniques.

This event is supported by the BBSRC Strategic Training Awards for Research Skills (STARS) grant (BB/P022766/1).

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Target audience
  • Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals
  • Please be aware that these courses are only free for University of Cambridge students. All other participants will be charged a registration fee in some form. Registration fees and further details regarding the charging policy are available here.
  • Further details regarding eligibility criteria are available here

Number of sessions: 2

# Date Time Venue Trainer
1 Thu 21 Jun 2018   09:30 - 17:00 09:30 - 17:00 Bioinformatics Training Room, Craik-Marshall Building map Matt Castle
2 Fri 22 Jun 2018   09:30 - 17:00 09:30 - 17:00 Bioinformatics Training Room, Craik-Marshall Building map Matt Castle
Topics covered

Bioinformatics, Data handling, Statistical calculation


After this course you should be able to:

  • Analyse datasets using classical statistical techniques (up to generalised linear models)
  • Know when each test is and is not appropriate

During this course you will learn about:

  • One and two sample hypothesis tests
  • Simple linear Regression
  • Linear Models
  • Generalised Linear Models
  • Model selection techniques

Presentations, demonstrations and practicals


Day 1 Topics
9:30 - 10:30 Lecture: Introduction to Statistics
10:30 - 12:30 Practical: R revision and simple hypothesis testing
12:30 - 13:30 Lunch (not provided)
13:30 - 14:30 Lecture: Univariate linear models
14:30 - 17:00 Practical: Univariate linear models
Day 2
9:30 - 10:30 Lecture: Multivariate linear models
10:30 - 12:30 Practical: Multivariate linear models
12:30 - 13:30 Lunch (not provided)
13:30 - 14:30 Lecture: Generalized linear models
14:30 - 16:30 Practical: Generalized linear models
16:30 - 17:00 Q&A and wrap-up
Registration Fees
  • Free for University of Cambridge students
  • £ 50/day for all University of Cambridge staff, including postdocs, and participants from Affiliated Institutions. Please note that these charges are recovered by us at the Institutional level
  • It remains the participant's responsibility to acquire prior approval from the relevant group leader, line manager or budget holder to attend the course. It is requested that people booking only do so with the agreement of the relevant party as costs will be charged back to your Lab Head or Group Supervisor.
  • £ 50/day for all other academic participants from external Institutions and charitable organizations. These charges must be paid at registration
  • £ 100/day for all Industry participants. These charges must be paid at registration
  • Further details regarding the charging policy are available here

Participants may find it useful to look over this R tutorial




A number of times per year

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