8 July 2016
on course website
Statistical Programming with R
R is rapidly becoming the standard platform for data analysis and has a number of advantages over other statistical software packages. A wide community of users contribute to R, enabling it to cover an enormous amount of statistical procedures, including many that are not covered in any other statistical program. Furthermore, it is highly flexible for programming purposes, for example when manipulating data or creating professional plots. However, R lacks standard menus, as in SPSS for example, from which to choose what statistical test to perform or which graph to create. As a consequence, R is more challenging to master. Therefore, this course offers an elaborate introduction into statistical programming in R. Students learn to operate R, make plots, fit, assess and interpret a variety of statistical models and do basic statistical programming. The topics in this course include regression models for linear, dichotomous, ordinal and multivariate data, and some basic bootstrapping and Monte Carlo simulation techniques.
The course deals with the following topics:
1. An introduction to the R environment.
2. Basic programming skills: data generation, manipulation, summaries and plotting.
3. Fitting linear models: regression and ANOVA.
4. Fitting generalized linear models (GLM): logistic and ordinal regression.
5. Fitting multivariate models: PCA, MANOVA, discriminant analysis and Repeated Measures.
6. Bootstrapping and Monte Carlo simulation.
Applied researchers and (master) students who already use statistical software and would like to learn to use, or improve their usage of the flexible R-environment. Understanding of basic statistical procedures such as t-tests, (M)AN(C)OVA, and regression is required.
Participants from a variety of fields, including sociology, psychology, education, human development, marketing, business, biology, medicine, political science, and communication sciences, will benefit from the course.
After registration we will ask you to briefly describe your statistical programming experience (none required) as well as your expectations from this course.
A maximum of 60 participants will be allowed in this course.
The course teaches students the necessary skills to understand how R works, and how to use R for a variety of statistical analysis of data in the behavioural and social sciences.
The skills addressed in this practical are:
? working with the R environment.
? using R-functions for data generation, manipulation and summaries.
? making high-quality plots.
? fitting and interpreting a variety of statistical models.
? Programming of simple bootstraps and Monte Carlo simulations.
EUR 800: Course + course materials + housing
EUR 600: Course + course materials
on course website