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Description

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 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 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.

Target Audience

• The course is open to graduate students and postdocs from all departments and affiliated institutions within the GSLS
• Many of the bespoke courses that we run choose to use this material as a key component of their courses. Please check to see if you have already attended, or will attend in the future a bespoke course run by us via your department before booking.

Topics Covered

During this course you will cover the following topics:

• One and two sample hypothesis tests
• ANOVA
• Simple linear Regression
• ANCOVA
• Linear Models
• Model selection techniques
• Power Analysis
• Multiple Comparison Methods

Format

There are two versions of this course:

1. Core Statistics: this course assumes participants are already familiar with the R language & software and comprises six 3-hour practical sessions.
2. Core Statistics with R Introduction: this course assumes that participants have little or no previous experience with the R language & software and comprises eight 3-hour practical sessions.

Each session is comprised of a computer practical interspersed with short lectures and presentations that are used to explain core ideas and prinicples.

The courses are run at a variety of locations throughout the University and at a range of times over the year. Some instances of this course are run in dedicated computer rooms whereas other instances are run in other training rooms and will require you to bring your own laptop. Please check the timetable for your specific instance of the course carefully and please ensure that you have fully functioning laptop if you book onto a laptop-only course.