This course provides an introduction to the statistical theory of sampling, parameter estimation and hypothesis testing. The class is taught either with Python or R examples (see the dates below for details). However, no prior programming experience is required.

Syllabus:

• random variables and distributions
• sampling distribution 
• central limit theorem
• standard deviation versus standard error
• confidence intervals
• hypothesis testing
  
  

Learning Outcomes:

On completion of this workshop you will be able to:

• Identify different statistical distributions
• Recognise sampling constraints and variability
• Employ skills to build confidence intervals
• Apply correct test statistics for hypothesis testing
• Assess numerical results to make statistical inferences

Dates & Booking Information

• Tuesday 03 May 2022, 09:30-12:30, MS Teams - Python Version

Please email the Graduate School with your CID number to book your place.