Introduction to R & Statistical Modelling in R

Venue: University of Manchester, M13 9PL.

Course Outline

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. 

Learning Outcomes

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.

Topics Covered

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.

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.

Assumed Knowledge

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

Each participant will need to bring their own laptop installed with the R software (which can be downloaded free for Linux, MacOS X or windows from

Fees (Registration before 28 August 2019)

Non Member £596+vat

RSS Fellow £507+vat

RSS CStat: also MIS, FIS & GradStat £478+vat

Fees (Registration on/after 28 August 2019)

Non Member £663+vat

RSS Fellow £563+vat

RSS CStat: also MIS, FIS & GradStat £530+vat

Multiple booking discounts available for bookings of 3 or more places - please contact for further information