Introduction to Sampling & Hypothesis Testing

Introduction to the course topics:

This course provides an introduction to the statistical theory of sampling, parameter estimation and hypothesis testing in R. 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, MS Teams

Additonal information, notes:

Please note that this course is offered in cooperation with the Imperial College London. Joining instructions for the course will be provided on booking.