no. | Section | Topic | size (MB) | time (min) |

1 | 2.1 | Definition of odds, and examples of sample spaces | 233 | 34 |

2 | 3.1 | Counting techniques | 128 | 22 |

3 | 3.1 | Combinations | 84 | 10 |

4 | 3.1 | Bridge Hand example of calculation using combinations | 30 | 6 |

5 | 3.1 | Birthday Problem and geometric series | 35 | 9 |

6 | 3.2 | The Binomial Theorem | 31 | 8 |

7 | 3.2 | Multinomial series and other series | 32 | 8 |

8 | | Waiting time problem: Return to Donnelly's example | 48 | 9 |

9 | 4.1,4.2 | The rules of probability | 211 | 39 |

10 | 4.3 | Intersection and Independence | 22 | 6 |

11 | 4.3 | Independence, part 2. | 48 | 12 |

12 | 4.3 | Mutual independence | 71 | 16 |

13 | 4.4 | An example using conditional probability | 171 | 26 |

14 | 4.5 | The multiplication rule | 114 | 20 |

15 | 4.5 | Bayes Rule | 62 | 11 |

16 | 5.1 | Definition of Random Variables | 122 | 22 |

17 | 5.1 | Cumulative Distribution Functions | 158 | 25 |

18 | 5.2 | The discrete uniform distribution | 133 | 24 |

19 | 5.3 | The Hypergeometric Distribution | 10 | 16 |

20 | 5.4 | The Binomial Distribution | 32 | 8 |

21 | 5.4 | A Binomial distribution example | 81 | 10 |

22 | 5.4 | The Statistician's stagger | 77 | 10 |

23 | 5.5 | The Negative binomial distribution | 90 | 9 |

24 | 5.5,5.6 | The Negative binomial 2 | 110 | 22 |

25 | 5.7 | The Poisson distribution | 112 | 22 |

26 | | A review of discrete distributions | 130 | 15 |

27 | 5.8 | The Poisson distribution 2 | 55 | 8 |

28 | 5.8 | The Poisson process 1 | 100 | 10 |

29 | 5.8 | The Poisson process 2 | 119 | 24 |

30 | 5.9 | Combining Models | 33 | 9 |

31 | | The Monty Hall Problem | 44 | 7 |

32 | | Monty Hall 2 | 27 | 6 |

33 | 7.2 | Expected Value | 105 | 15 |

34 | 7.2,7.3 | Expected value: example and interpretation through histograms | 38 | 8 |

35 | 7.4 | Expected value of Poisson | 13 | 3 |

36 | 7.4 | Expected value of binomial | 25 | 7 |

37 | 7.2 | Expected value of g(X) | 69 | 15 |

38 | 7.2 | Laws of Expected value and E(g(X)) part 2 | 40 | 11 |

39 | 7.4 | Variance | 43 | 11 |

40 | 7.4 | Variance 2 | 115 | 20 |

41 | 7.4 | Laws of Variance | 47 | 8 |

42 | 7.4 | Variance of the Poisson | 38 | 8 |

43 | 7.4 | Variance of binomial | 28 | 6 |

44 | | Review of expected value and variance | 78 | 20 |

45 | 7.5 | Moment generating functions | 83 | 19 |

46 | 7.5 | Uses of the moment generating function | 180 | 25 |

47 | 8.1,8.2 | Joint distributions, independence and the multinomial distributions | 29 | 41 |

48 | 8.2 | Multinomial 2 | 23 | 27 |

49 | 8.3 | Markov Chains | 12 | 13 |

50 | 8.3 | Markov Chains 2 | 22 | 27 |

51 | 8.4 | Expected value of a Product | 30 | 38 |

52 | 8.4 | Covariance | 17 | 22 |

53 | 8.4 | Covariance 2 and correlation | 29 | 38 |

54 | 8.5 | Indicator Random variables and their use | 30 | 39 |

55 | 9.1 | Continuous Distributions | 25 | 26 |

56 | 9.1 | Continuous Distributions 2 | 27 | 31 |

57 | 9.1 | Distribution of a Function of a Random Variable | 47 | 16 |

58 | 9.1 | Expected Value and Variance for Continuous Distributions | 47 | 25 |

59 | 9.4 | Inverse Transform: generating random variables with a given distribution | 48 | 26 |

60 | 9.3 | The exponential Distribution | 36 | 26 |

61 | 9.3 | The Exponential Distribution and the Poisson Process | 40 | 23 |

62 | 9.5 | The Normal Distribution | 85 | 50 |

63 | 9.6 | The Central Limit Theorem (video, powerpoint show) | 18, 105 | 23 |

64 | 9.6 | The normal distribution and the Central Limit Theorem | 91 | 27 |

65 | 9.6 | Normal approximations | 68 | 27 |