Core Statistics

BeginnersPrerequisites

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 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 included as part of several DTP programmes as well as other departmental training within the university (potentially under a different name) so participants who have attended statistics training elsewhere should check before applying.
• Please be aware that this course is only free for University of Cambridge students. Any University of Cambridge staff, including postdocs, wishing to attend will be charged a registration fee. A purchase order will need to be provided.

This course requires users to be familiar with the R language. Attending an introductory R course is essential if you do not have a working knowledge of the R language already.

Number of sessions: 6

Learning Objectives After this course you should be able to:

1. Analyse datasets using standard statistical techniques
2. Know when each test is and is not appropriate

During this course you will learn about:

• One and two sample hypothesis tests
• ANOVA
• Simple linear Regression
• ANCOVA
• Linear Models
• Model selection techniques
• Power Analyses

The course is primarily based around computer practicals interspersed with short lectures and presentations used to explain core ideas and principles.

The course consists of six 3-hour sessions spread over three weeks. If you book onto this course you must attend all of the sessions as detailed below.

Six 3-hour sessions

Several times per term

• Mathematics & Statistics
• Research Skills & Techniques