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- 1.1 The Monty Hall Problem
- 1.2 Decision Making Under Uncertainty
- 1.3 Uncertainty in the News
- 1.4 Simplicity vs. Complexity - The Need for Models
- 1.5 Safe to Assume- Beware, When Model Assumptions Go Wrong!
- 1.6 Roadmap of the Course
- 1.7 Week One Summary and Key Takeaways
- 2.1 Probability Principles
- 2.2 Simple Probability Distributions
- 2.3 Expectation of Random Variables
- 2.4 Bayesian Updating
- 2.5 Parameters
- 2.6 The Distribution Zoo
- 2.7 Week Two Summary and Key Takeaways
- 3.1 Classify Your Variables!
- 3.2 Data Visualisation
- 3.3 Descriptive Statistics - Measures of Central Tendency
- 3.4 Descriptive Statistics - Measures of Spread
- 3.5 The Normal Distribution
- 3.6 Variance of Random Variables
- 3.7 Week Three Summary and Key Takeaways
- 4.1 Introduction to Sampling
- 4.2 Random Sampling
- 4.3 Further Random Sampling
- 4.4 Sampling Distributions
- 4.5 Sampling Distribution of the Sample Mean
- 4.6 Confidence
- 4.7 Week Four Summary and Key Takeways
- 5.1 Satistical Juries
- 5.2 Type I and Type II errors
- 5.3 P-values, Effect Size and Sample Size Influences
- 5.4 Testing a Population Mean Claim
- 5.5 The Central Limit Theorem
- 5.6 Proportions：Confidence Intervals and Hypothesis Testing
- 5.7 Week Five Summary and Key Takeaways
- 6.1 Decesion Tree Analysis
- 6.2 Risk
- 6.3 Linear Regression
- 6.4 Linear Programming
- 6.5 Monte Carlo Simulation
- 6.6 Overview of the Course and Next Steps