Data Analysis in R

With the increasing use of statistical languages like R in data analysis, now is the time to get to grips with them! This course focuses on understanding statistical models and analysing the results whilst learning to work with R. As well as introducing the software to newcomers, it presents basic and more advanced statistics using an overarching framework of the generalized linear model. Every day consists of short lectures with examples, and exercises in which you apply what you have learnt. The focus in the exercises and assignment is the coding in R and how to apply and to interpret generalized linear regression models. After class, you work on an assignment in which you integrate what you’ve learnt in the exercises. This assignment will be graded.

Course leader

Dr Meike Morren

Target group

The course is highly intensive both in programming and statistics, which means it will be challenging to those who lack knowledge in both fields. Preferably, you have completed an undergraduate course in statistics, an acquaintance with basic linear algebra, the fundamentals of hypothesis testing, linear regression analysis and statistical tests such as the t-test. Although we will briefly go over these topics again to refresh your memory, we do not recommend this course to those who learn will these concepts for the first time. For people with programming experience (other languages such as Python), it might be possible to step in with little prior knowledge of statistics.

Course aim

By the end of this course, students will be able to: - Evaluate the quality of quantitative data sources - Choose the appropriate method for analysis, depending on the data source - Conduct various statistical tests - Analyse data using generalised linear framework - Analyse multi-item scales using principal components and factor analysis - Have developed their skills in R programming