Week 3: Probability

Practical Skills: Data Organisation

Slides

Probability

• Basic probability concepts
• Combinatorics
• Conditional probability

Data organisation

• Tidy data
• Pivotting data
• Joining two datasets

Performance Outcomes

You should feel confident to perform the following outcomes:

Probability

• Define and interpret fundamental probability concepts
• Simulate random experiments, like coin tosses and dice rolls
• Distinguish between empirical and theoretical probability
• Identify and describe sample spaces and events
• Calculate probabilities for simple and compound events
• Apply basic probability rules, including addition and multiplication rules
• Recognize and use the concepts of mutually exclusive and independent events
• Explain and illustrate the law of large numbers
• Calculate conditional probabilities and understand their applications
• Use probability to solve real-world problems and make informed decisions

Data wrangling

• Recognize the characteristics of tidy data
• Pivot data into longer or wider format using R
• Join two or more datasets using R

Resources

If you don’t feel yet confident after going through the teaching materials or would like further resources, then you are also encouraged to go through the following materials.

• R for Data Science (2nd Edition)
  • Chapter 5: Data tidying
  • Chapter 19: Joins
• OpenIntro Statistics (4th Edition)
  • Chapter 3: Probability
• Data tidying with tidyr Cheatsheet

This website, made using Quarto and hosted via GitHub Pages, is brought to you by the ANU Research School of Finance, Actuarial Studies and Statistics.