Random Variables

Random Variables

Eddie Woo via YouTube Direct link

Variance (4 of 4: Proof of two formulas)

21 of 38

21 of 38

Variance (4 of 4: Proof of two formulas)

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Classroom Contents

Random Variables

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  1. 1 What are Continuous Random Variables? (1 of 3: Relation to discrete data)
  2. 2 What are Continuous Random Variables? (2 of 3: Why we need different tools)
  3. 3 What are Continuous Random Variables? (3 of 3: Conditions for a Probability Density Function)
  4. 4 Probability Density Functions (1 of 7: Meeting the conditions)
  5. 5 Probability Density Functions (2 of 7: Evaluating a probability)
  6. 6 Probability Density Functions (3 of 7: Unknowns in the function)
  7. 7 Probability Density Functions (4 of 7: Domain restrictions)
  8. 8 Probability Density Functions (5 of 7: Reviewing integration skills)
  9. 9 Probability Density Functions (6 of 7: Unbounded integrals)
  10. 10 Probability Density Functions (7 of 7: Uniform distributions)
  11. 11 Mode & Median of a Continuous Probability Distribution
  12. 12 Locating Boundaries of a Distribution from its Median
  13. 13 Finding Percentiles of a Continuous Probability Distribution
  14. 14 Expected Value of a Continuous Distribution (1 of 2: Relation to discrete data)
  15. 15 Expected Value of a Continuous Distribution (2 of 2: Worked example)
  16. 16 Probability Density Functions Q&A (1 of 2: Evaluating probabilities)
  17. 17 Probability Density Functions Q&A (2 of 2: Two approaches to an unbounded probability)
  18. 18 Variance (1 of 4: Introducing the formulas)
  19. 19 Variance (2 of 4: Worked example with first formula)
  20. 20 Variance (3 of 4: Worked example with second formula)
  21. 21 Variance (4 of 4: Proof of two formulas)
  22. 22 Cumulative Distribution Function (2 of 3: Evaluating probabilities)
  23. 23 Cumulative Distribution Function (1 of 3: Definition)
  24. 24 Cumulative Distribution Function (3 of 3: Locating quantiles)
  25. 25 Probability Density Functions Q&A (1 of 2: Evaluating probabilities)
  26. 26 Probability Density Functions Q&A (2 of 2: Two approaches to an unbounded probability)
  27. 27 What is the Normal Distribution?
  28. 28 Normally Distributed Empirical Data (1 of 2: Comparing marathon times)
  29. 29 Normally Distributed Empirical Data (2 of 2: Calculating population proportions)
  30. 30 Probability Density Function of the Normal Distribution
  31. 31 Trapezoidal Rule on Normal Distribution (1 of 2: Reviewing the formula)
  32. 32 Trapezoidal Rule on Normal Distribution (2 of 2: Verifying empirical result)
  33. 33 Integrating Normal Distribution with Technology (1 of 2: One-sided inequality)
  34. 34 Integrating Normal Distribution with Technology (2 of 2: Contrasting populations)
  35. 35 Statistical Tables (1 of 2: How to interpret values)
  36. 36 Statistical Tables (2 of 2: Combining results)
  37. 37 Variance on a Modified Distribution (1 of 2: Worked example)
  38. 38 Variance on a Modified Distribution (2 of 2: Investigating the modifications)

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