The purpose of this course is to introduce participants to the R environment for statistical computing. Day 1 of the course focuses on entering, working with and visualising data in R. Day 2 focuses on regression modelling in R, including linear, general linear, logistic and survival models.

### Target Audience

This course is ideally suited to anyone who:

- is familiar with basic statistical methods (e.g. t-tests, boxplots) and who want to implement these methods using R.
- has used menu-driven statistical software (e.g. SPSS, Minitab) and who want to investigate the flexibility offered by a command line package such as R.
- is already familiar with basic statistical methods in R and who wish to extend their knowledge to regression involving multiple predictor variables, binary, categorical and survival response variables.
- is familiar with regression methods in menu-driven software (e.g. SPSS, Minitab) and who wish to migrate to using R for their analyses.

The course requires familiarity with basic statistical methods (e.g. t-tests, box plots) but assumes no previous knowledge of statistical computing.

Topics covered in Day 1 include: entering data and obtaining help in R; working with data in R; summarising data graphically and numerically in R; basic hypothesis tests in R.

Topics covered in Day 2 include: the linear model in R; the general linear model in R; logistic regression in R; survival models in R.

By the end of Day 1, participants will be able to use R to:

- Perform data entry from a variety of sources (e.g. Excel and SPSS spreadsheets).
- Produce simple variable summaries (e.g. means, variances, quartiles) and graphical displays (e.g. histograms, box plots, scatter plots).
- Find further information using the help system and online resources.
- Perform simple hypothesis tests on one or two variables; appropriately interpreting results and checking validity of assumptions.

By the end of Day 2, participants will be able to:

- Fit regression models in R between a response variable (including continuous, binary, categorical and survival responses) and a set of possible predictor variables
- Make appropriate assumptions about the structure of the data in a regression model and check the validity of these assumptions in R.