Statistical Methods: Monte Carlo Simulations with R

In Person

This advanced workshop will focus on outlining the importance of and developing the theoretical and practical skills necessary for Monte Carlo simulations in R.  

The session will begin with a brief overview of statistics, and go on to cover the use of simulation modelling and specifically Monte Carlo simulations for hypothesis testing. This will focus on: 

• Constructing simulation models in R 
• Hypothesis testing with Monte Carlo simulations 
• Modelling complex numerical problems 

The workshop will ultimately build on an understanding of basic statistical analysis and show how more complex statistical analyses and simulations can be used in R. The relatively advanced techniques and theory behind all of this are highly applicable to wider concepts and will allow attendees to apply and build upon the core concepts in their own research. 

This is an advanced workshop. A general understanding of R and statistical analyses is assumed. This assumed understanding is at the level covered in the Introduction to Statistical Analysis.  Participation in that introductory session is advised before undertaking this session. The Tidyverse package should be downloaded and installed before the workshop.     

Those who have registered to take part will receive an email with full details on how to get ready for this course. 

After taking part in this event, you may decide that you need some further help in applying what you have learnt to your research. If so, you can book a Data Surgery meeting with one of our training fellows. 

More details about Data Surgeries. 

If you’re new to this training event format, or to CDCS training events in general, read more on what to expect from CDCS training. Here you will also find details of our cancellation and no-show policy, which applies to this event. 

If you're interested in other training on data analysis, statistics, and machine learning have a look at the following: 

• Digital Method of the Month: Statistics 
• Introduction to Statistics and Descriptive Statistics 
• Finding Patterns Across Data
• Digital Method of the Month: Machine Learning 
• Introduction to Machine Learning 
• Statistical Methods: Null-hypothesis Testing with R
• Systematic Data Cleaning in Python
• Regression and Mixed Effects Modelling with R
• AI and Ethics
• Statistical Methods: Principal Component Analysis with R 

Return to the Training Homepage to see other available events.