Dealing with Uncertainty and Complexity in a Chaotic World-Week One
Welcome!
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1.1 The Monty Hall Problem
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1.1 The Monty Hall Problem
1.2 Decision Making Under Uncertainty
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1.2 Decision Making Under Uncertainty
1.3 Uncertainty in the News
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1.3 Uncertainty in the News
1.4 Simplicity vs. Complexity - The Need for Models
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1.4 Simplicity vs. Complexity - The Need for Models
1.5 Safe to Assume? Beware, When Model Assumptions Go Wrong!
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1.5 Safe to Assume? Beware, When Model Assumptions Go Wrong!
1.6 Roadmap of the Course
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1.6 Roadmap of the Course
Week One Summary and Key Takeaways
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Quantifying Uncertainty With Probability-Week Two
2.1 Probability Principles
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2.1 Probability Principles
2.2 Simple Probability Distributions
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2.2 Simple Probability Distributions
2.3 Expectation of Random Variables
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2.3 Expectation of Random Variables
2.4 Bayesian Updating
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2.4 Bayesian Updating
2.5 Parameters
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2.5 Parameters
2.6 The Distribution Zoo
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2.6 The Distribution Zoo
Week Two Summary and Key Takeaways
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Describing The World The Statistical Way-Week Three
3.1 Classify Your Variables!
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3.1 Classify Your Variables!
3.2 Data Visualisation
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3.2 Data Visualisation
3.3 Descriptive Statistics - Measures of Central Tendency
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3.3 Descriptive Statistics - Measures of Central Tendency
3.4 Descriptive Statistics - Measures of Spread
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3.4 Descriptive Statistics - Measures of Spread
3.5 The Normal Distribution
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3.5 The Normal Distribution
3.6 Variance of Random Variables
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3.6 Variance of Random Variables
Week Three Summary and Key Takeaways
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On Your Marks, Get Set, Infer!-Week Four
4.1 Introduction to Sampling
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4.1 Introduction to Sampling
4.2 Random Sampling
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4.2 Random Sampling
4.3 Further Random Sampling
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4.3 Further Random Sampling
4.4 Sampling Distributions
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4.4 Sampling Distributions
4.5 Sampling Distribution of the Sample Mean
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4.5 Sampling Distribution of the Sample Mean
4.6 Confidence Intervals
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4.6 Confidence Intervals
Week Four Summary and Key Takeaways
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To p Or Not To p?-Week Five
5.1 Statistical Juries
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5.1Statistical Juries
5.2 Type I and Type II errors
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5.2 Type I and Type II errors
5.3 P-values, Effect Size and Sample Size Influences
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5.3 P-values, Effect Size and Sample Size Influences
5.4 Testing a Population Mean Claim
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5.4 Testing a Population Mean Claim
5.5 The Central Limit Theorem
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5.5 The Central Limit Theorem
5.6 Proportions: Confidence Intervals and Hypothesis Testing
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5.6 Proportions: Confidence Intervals and Hypothesis Testing
Week Five Summary and Key Takeaways
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Applications-Week Six
6.1 Decision Tree Analysis
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6.1 Decision Tree Analysis
6.2 Risk
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6.2 Risk
6.3 Linear Regression
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6.3 Linear regression
6.4 Linear Programming
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6.4 Linear Programming
6.5 Monte Carlo Simulation
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6.5 Monte Carlo Simulation
6.6 Overview of the Course and Next Steps
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