Introduction to Sampling & Hypothesis Testing (R Version)

Introduction to the course topics:

This course provides an introduction to the statistical theory of sampling, parameter estimation and hypothesis testing. The class will be taught with R examples. 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

Course aims:

• 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

Participation requirements:

none

Technical requirements:

Laptop/tablet with stable internet connection, microphone, webcam, Zoom (in most cases)

Additonal information, notes:

Please note that this course is offered in cooperation with the Imperial College London. If you want to register for this course, please enter in the following link: Book your place here

Information about the trainer:

Dr. John Pinney is Royal Society University Research Fellow in the Theoretical Systems Biology group at Imperial College London. More info here.