Introduction to R & Regression Modelling in R

Date:

01/11/2017 - 02/11/2017

Organised by:

RSS

Presenter:

Dr Paul Baxter

Level:

Intermediate (some prior knowledge)

Contact:

Tessa Pearson training@rss.org.uk 02076143947

Map:

View in Google Maps  (EC1Y 8LX)

Venue:

12 Errol Street London

Description:

Location: Royal Statistical Society, 12 Errol Street, London EC1Y 8LX
CPD: 12 hours
Level: Foundation

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 http://www.stats.bris.ac.uk/R/)

Cost:

£450

Website and registration:

Region:

Greater London

Keywords:

Quantitative Data Handling and Data Analysis, Regression Methods, ICT and Software, Quantitative Software, R

Related publications and presentations:

Quantitative Data Handling and Data Analysis
Regression Methods
ICT and Software
Quantitative Software
R

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