This course offers an elaborate introduction to statistical programming with R. Students learn to operate R, form pipelines for data analysis, make high quality graphics, fit, assess, and interpret a variety of statistical models, and do advanced statistical programming. The statistical theory in this course covers t-testing, regression models for linear, dichotomous, ordinal, and multivariate data, statistical inference, statistical learning, bootstrapping, and Monte Carlo simulation techniques.
R is a very popular and powerful platform for data manipulation, visualization, and analysis and has a number of advantages over other statistical software packages. A wide community of users contribute to R, resulting in broad coverage of statistical procedures, including many that are not available in any other statistical program. However, R lacks standard GUI menus from which to choose what statistical test to perform or which graph to create. Consequently, R is more challenging to master. This course will help flatten the learning curve for those who wish to begin working with R by offering an elaborate introduction to statistical programming in R.
In this course we will cover the following topics:
- An introduction to the R environment
- Basic to advanced programming skills: data generation, manipulation, pipelines, summaries, and plotting
- Fitting statistical models: estimation, prediction, and testing
- Drawing statistical inference from data
- Basic statistical learning techniques
- Bootstrapping and Monte Carlo simulation
The course starts at a very basic level and builds up gradually. So, no previous experience with R is required. At the end of the week, participants will master advanced programming skills with R.
Application deadline: 20 June 2022
This course is part of a series of 5 courses in the Summer School Data Science specialisation taught by UU’s department of Methodology & Statistics.