Introduction to Statistical Analysis.

This course provides a refresher on the foundations of statistical analysis. Practicals are conducted using the ‘Shiny’ package; which provides an accessible interface to the R statistical language.

Note that this is not a course for learning about the R statistical language itself. If you wish to learn more about R, please see other courses at the University of Cambridge

An Introduction to Solving Biological Problems with R

Authors

Dominique-Laurent Couturier
Mark Dunning

(Acknowledgements: Robert Nicholls, Matt Eldridge, Sarah Vowler, Deepak Parashar, Sarah Dawson, Elizabeth Merrell)

Aims

During this course you will learn about:

Different types of data, distributions and structure within data
Summary statistics for continuous and discrete data
Formulating a null hypothesis
Assumptions of one-sample and two-sample t-tests
Interpreting the result of a statistical test
Statistical tests of categorical variables (Chi-squared and Fisher’s exact tests)
Non-parametric versions of one- and two-sample tests (Wilcoxon tests)

We will not cover ANOVA or linear regression here but these are the topics of a more advanced course

Learning Objectives

After this course you should be able to:-

State the assumptions required for a one-sample and two-sample t-test and be able to interpret the results of such a test
Know when to apply a paired or independent two-sample t-test
To perform simple statistical calculations using the online app
Understand the limitations of the tests taught within the course
Know when more complex statistical methods are required

Course Materials

Software Requirements

You will need an internet connection in order to run the practicals and examples

Feedback

Feedback form for course run on 23 October 2017