Introduction to R & Statistical Modelling in R
Date:
13/05/2021 - 14/05/2021
Organised by:
Royal Statistical Society
Presenter:
Dr Colin Gillespie or Dr Jamie Owen
Level:
Entry (no or almost no prior knowledge)
Contact:
Venue: Online
Description:
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 and general linear models
Learning Outcomes
By the end of Day 1, participants will be able to use R to:
- Direct themselves around the R interface in an efficient way
- Import and export their own data from spreadsheets and a number of other data storages to R
- Summarise the data with R's built-in summary statistic functions
- Plot data in interesting ways
- manipulate data in ways such that they can efficiently analyse data
By the end of Day 2, participants will be able to:
- Have a thorough understanding of popular statistical techniques
- Have the skills to make appropriate assumptions about the structure of the data and check the validity of these assumptions in R
- Be able to fit regression models in R between a response variable
- Understand how to apply said techniques to their own data using R's common interface to statistical functions
- Be able to cluster data using standard clustering techniques
Topics Covered
Topics covered in Day 1 include:
- Introduction to R: A brief overview of the background and features of the R statistical programming system
- Data entry: A description of how to import and export data from R
- Data types: A summary of R's data types
- R environment: A description of the R environment including the R working directory, creating/using scripts, saving data and results
- R graphics: Creating, editing and storing graphics in R
- Summary statistics: Measures of location and spread
- Manipulating data in R: Describing how data can be manipulated in R using logical operators
- Vector operations: Details of R's vectors operations
Topics covered in Day 2 include:
- Basic hypothesis testing: Examples include the one-sample t-test, one-sample Wilcoxon signed-rank test, independent two-sample t-test, Mann-Whitney test,teo-sample t-test for paired samples. Wilcoxon signed-rank test
- ANOVA tables: One-way and two-way tables
- Simple and multiple linear regression: Including model diagnostics
- Clustering: Hierarchical clustering, k-means
- Principle components analysis: Plotting and scaling data
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 would like 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:
£588 - £816 (inc VAT)
Website and registration:
Region:
Greater London
Keywords:
Qualitative Data Handling and Data Analysis, Python, R environment , Visualising data , Regression Modelling , Linear models
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