This is a 2 day course from 22nd - 23rd October 2018.

R is both a statistical language and a major modern statistical software product, providing the main route to dissemination of recent statistical methodology which is free to download and extendable. 

This module introduces R, explains the R programming language, and introduces a wide range of statistical techniques available in R.

Important please note:

We will make every attempt to accommodate Lancaster University staff and postgraduate research students on our courses. However, if a course becomes fully booked we reserve the right to give priority to students on the MSc in Statistics, MSc in Data Science, and external participants.

Details of course fees.

Payment: Once you have registered, please pay at the online shop 

https://online-payments.lancaster-university.co.uk/product-catalogue/courses/mathematics-and-statistics/short-courses-and-cpd/applied-statistics-psc-short-courses-20182019

Accommodation Details: Can be found here.

Cancellation Policy

Registrations are transferable to another course or individual at any time. Full refunds will be given for cancellation 10 or more working days before the course start date. Otherwise the full course fee will be charged.

Topics covered include: an introduction to the R language, reading data, data description and graphics, the generalised linear model (GLM), logistic regression, survival analysis, multivariate techniques, the use of R libraries and resources in R.

In summary, the following topics will be covered:

1. manipulation and management of data in the R environment
2. summarising data numerically and graphically
3. fitting linear models in R
4. statistical analysis using R
5. functions, iterations and other programming structures in R

Learning: Students will learn through the application of concepts and techniques covered in the module to real data sets.

Successful students will be able to use R to:

· be able to produce basic statistics and create graphs
· write functions, iterative loops and conditional execution
· fit linear models.

 
Bibliography:

Crawley, M.J. (2005) Statistics: an introduction using R Wiley, New York.

Dalgaard, P. (2002) Introductory Statistics with R. Springer. Faraway, J (2004) Linear models with R. Chapman and Hall.

Maindonald, J. and Braun, J. (2003) Data analysis and graphics using R. Cambridge University Press.

Venables, W and Ripley, B (2002) Modern Applied Statistics with S . Springer.

Venables, W and Smith, D (2002) An introduction to R. Network Theory Ltd.