Statistics for Biologists in R

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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.

There are three core goals for this course:

1. Use R confidently for statistics and data analysis
2. Be able to analyse datasets using standard statistical techniques
3. Know which tests are and are not appropriate

R is a free, software environment for statistical and data analysis, with many useful features that promote and facilitate reproducible research.

In this course, we introduce the R language, and cover basic data manipulation and plotting. We then move on to 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.

• The course is open to graduate students and postdocs from all departments and affiliated institutions within the GSLS
• This course is part of several DTP programmes as well as other departmental training so students who have attended statistics training on these courses should not apply.

No prior experience with R is required, nor is any previous statistics knowledge assumed.

Number of sessions: 8

Learning Objectives After this course you should be able to:

1. Use R and RStudio to manipulate data, produce scientific figures and perform exploratory data analyses
2. Analyse datasets using standard statistical techniques
3. Know when each test is and is not appropriate

During this course you will learn about:

• The RStudio interface to R
• Basic data manipulation in R (importing data, using built in functions and plotting)
• One and two sample hypothesis tests
• ANOVA
• Simple linear Regression
• ANCOVA
• Linear Models
• Generalised Linear Models
• Model selection techniques

The course is primarily based around computer practicals interspersed with short lectures and presentations used to explain core ideas and principles. Participants are welcome to bring their own laptops to work on.

The course is split over eight 3 hour sessions all held in the eLearning Suite in the School of Clinical Medicine. If you book onto this course you must attend all of the sessions as detailed below. Failure to attend a session or cancellation of your place less than 48 hours before the start of the first session will result in an administrative charge of £50.

Eight three hour sessions

Several times per term

• Mathematics & Statistics
• Research Skills & Techniques