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概率论与数理统计

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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
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