A system for statistical analysis using R and the R-commander

4 - 8 July 2016

methods@manchester Summer School at the University of Manchester

 

Overview

This course provides a system for analysing a wide variety of data and research designs using modern methods and software.  Generalized linear models are applied to analyse continuous, count and categorical data.  The course covers the whole process of defining a research question, selecting and running an appropriate analytical technique and interpreting the results using graphical displays. A significant component is the practical sessions where participants analyse numerous data sets using the R statistical software.  Full training and software is provided for all participants.  The course also covers the coding and manipulation of data, the inclusion of categorical explanatory variables, interaction terms, model diagnostics, variable selection and editing graphical displays.  The aim of the course is to provide a complete set of tools for analysing a wide variety of data using a coherent statistical theory and modern methods which are powerful and relatively easy to use. 

 

Course objectives

To introduce a theoretically consistent system of analysis that can be used to analyse a wide variety of data and research designs.  Practical sessions will enable participants to analyse examples of all techniques using R and the R-commander.  

 

Course timetable

Day one - Setting Up The System ...

Session 1   Introduction to the course (a system of analysis)

Session 2   Software (R, Rcmdr, R-studio)

Session 3   Data Coding and structure

Session 4   Practical session - Using R, the Rcmdr and the R-studio

Day two - A System of Analysis ...

Session 5   Defining Research Questions

Session 6   Selecting Analytical Techniques

Session 7   Interpreting Results (effect displays)

Session 8   Exercises (analysing different types of data)

Day three - Modelling Numeric Data ...

Session 9   Modelling continuous data (OLS, ANOVA...)

Session 10  Categorical explanatory variables

Session 11  Modelling count data

Session 12  Modelling frequency tables

Day four - Modelling Categorical Data ...

Session 13  Logistic Regression

Session 14  proportional odds models

Session 15  multinomial regression

Session 16  Exercises - modelling categorical data

Day five - Challenges

Session 17  Diagnostics and data transformation

Session 18  Exercise on diagnostics

Session 19  Variable Selection

Session 20  Editing graphics (TikZ and LaTeX)

 

Course presenter

The course will be presented by Dr Graeme Hutcheson.

Graeme Hutcheson has worked in a number of social science disciplines for over 20 years and has written numerous books and academic papers dealing specifically with research methodology and statistics.  His current interest is with the application and promotion of a unified system of analysis that applies to a wide range of research problems and can be learned within the time-frame available to a typical postgraduate student.  He is currently working at Manchester University and also runs the Manchester R group, which promotes the use of 'R', the statistical analysis system.

 

Prior or recommended knowledge/reading/skills

No previous knowledge of statistics or use of the software is required, although some experience with using data tables and basic statistical terminology may be helpful (for example, means, standard deviations, p-values)

 

Software to be used

R, Rcmdr and R-studio.  These software packages are all open-source and will be provided as part of the course.  Participants are encouraged to bring their own lap-tops (linux, windows or Mac) - all required software will be loaded on the first day of the course.

 

Booking

To book your place and have an invoice submitted to your institution submit your details here.

To book your place and pay by credit or debit card please visit our e-store

 

Course fees

Students £600   |   Other attendees £900

 

 

Methods@Manchester | G9 Humanities Bridgeford Street | University of Manchester | Manchester | M13 9PL

Tel 0161 275 0796

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