1. Discrete Random Variables

2. Discrete random variables For a discrete random variable X the probability distribution is described by the probability function, p(x), which has the following properties :

3. Comment: For a discrete random variable the number of possible values (i.e. x such that p(x) > 0) is either finite or countably infinite (in a 1-1 correspondence with positive integers.)

4. Recall p(x) = P[X = x] = the probability function of X. This can be defined for any random variable X. For a continuous random variable p(x) = 0 for all values of X. Let S X ={x| p(x) > 0}. This set is countable (i. e. it can be put into a 1-1 correspondence with the integers} S X ={x| p(x) > 0}= {x 1, x 2, x 3, x 4, …} Thus let

5. Proof: (that the set S X ={x| p(x) > 0} is countable) (i. e. can be put into a 1-1 correspondence with the integers} S X = S 1  S 2  S 3  S 3  … where i. e.

6. Thus the elements of S X = S 1  S 2  S 3  S 3  … can be arranged {x 1, x 2, x 3, x 4, … } by choosing the first elements to be the elements of S 1, the next elements to be the elements of S 2, the next elements to be the elements of S 3, the next elements to be the elements of S 4, etc This allows us to write

7. A Discrete Random Variable A random variable X is called discrete if That is all the probability is accounted for by values, x, such that p(x) > 0.

8. Discrete Random Variables For a discrete random variable X the probability distribution is described by the probability function p(x), which has the following properties

9. Graph: Discrete Random Variable p(x)p(x) a b

10. Some Important Discrete distributions

11. The Bernoulli distribution

12. Suppose that we have a experiment that has two outcomes 1.Success (S) 2.Failure (F) These terms are used in reliability testing. Suppose that p is the probability of success (S) and q = 1 – p is the probability of failure (F) This experiment is sometimes called a Bernoulli Trial Let Then

13. The probability distribution with probability function is called the Bernoulli distribution p q = 1- p

14. The Binomial distribution

15. Suppose that we have a experiment that has two outcomes (A Bernoulli trial) 1.Success (S) 2.Failure (F) Suppose that p is the probability of success (S) and q = 1 – p is the probability of failure (F) Now assume that the Bernoulli trial is repeated independently n times. Let Note: the possible values of X are {0, 1, 2, …, n}

16. For n = 5 the outcomes together with the values of X and the probabilities of each outcome are given in the table below: FFFFF 0 q 5 SFFFF 1 pq 4 FSFFF 1 pq 4 FFSFF 1 pq 4 FFFSF 1 pq 4 FFFFS 1 pq 4 SSFFF 2 p 2 q 3 SFSFF 2 p 2 q 3 SFFSF 2 p 2 q 3 SFFFS 2 p 2 q 3 FSSFF 2 p 2 q 3 FSFSF 2 p 2 q 3 FSFFS 2 p 2 q 3 FFSSF 2 p 2 q 3 FFSFS 2 p 2 q 3 FFFSS 2 p 2 q 3 SSSFF 3 p 3 q 2 SSFSF 3 p 3 q 2 SSFFS 3 p 3 q 2 SFSSF 3 p 3 q 2 SFSFS 3 p 3 q 2 SFFSS 3 p 3 q 2 FSSSF 3 p 3 q 2 FSSFS 3 p 3 q 2 FSFSS 3 p 3 q 2 FFSSS 3 p 3 q 2 SSSSF 4 p 4 q SSSFS 4 p 4 q SSFSS 4 p 4 q SFSSS 4 p 4 q FSSSS 4 p 4 q SSSSS 5 p 5

17. For n = 5 the following table gives the different possible values of X, x, and p(x) = P[X = x] x012345 p(x) = P[X = x] q5q5 5pq 4 10p 3 q 2 10p 2 q 3 5p4q5p4qp5p5

18. For general n, the outcome of the sequence of n Bernoulli trails is a sequence of S’s and F’s of length n. SSFSFFSFFF…FSSSFFSFSFFS The value of X for such a sequence is k = the number of S’s in the sequence. The probability of such a sequence is p k q n – k ( a p for each S and a q for each F) There are such sequences containing exactly k S’s is the number of ways of selecting the k positions for the S’s. (the remaining n – k positions are for the F’s

19. Thus These are the terms in the expansion of (p + q) n using the Binomial Theorem For this reason the probability function is called the probability function for the Binomial distribution

20. Summary We observe a Bernoulli trial (S,F) n times. where Let X denote the number of successes in the n trials. Then X has a binomial distribution, i. e. 1. p = the probability of success (S), and 2. q = 1 – p = the probability of failure (F)

21. Example A coin is tossed n= 7 times. Thus Let X denote the number of heads (H) in the n = 7 trials. Then X has a binomial distribution, with p = ½ and n = 7.

22. x01234567 p(x)p(x) 1 / 128 7 / 128 21 / 128 35 / 128 21 / 128 7 / 128 1 / 128 p(x)p(x) x

23. Example If a surgeon performs “eye surgery” the chance of “success” is 85%. Suppose that the surgery is perfomed n = 20 times Thus Let X denote the number of successful surgeries in the n = 20 trials. Then X has a binomial distribution, with p = 0.85 and n = 20.

24. p(x)p(x) x

25. The probability that at least sixteen operations are successful = P[X ≥ 16] = p(16) + p(17) + p(18) + p(19) + p(20) = 0.1821 + 0.2428 + 0.2293 + 0.1368 + 0.0388 = 0.8298

26. Other discrete distributions Poisson distribution Geometric distribution Negative Binomial distribution Hypergeometric distribution

27. The Poisson distribution Suppose events are occurring randomly and uniformly in time. Let X be the number of events occuring in a fixed period of time. Then X will have a Poisson distribution with parameter.

28. Some properties of the probability function for the Poisson distribution with parameter.

29. 2.If is the probability function for the Binomial distribution with parameters n and p, and we allow n → ∞ and p → 0 such that np = a constant (= say) then

30. Proof: Suppose

31. Now Now using the classic limit

32. Graphical Illustration Suppose a time interval is divided into n equal parts and that one event may or may not occur in each subinterval. time interval n subintervals - Event occurs - Event does not occur As n→∞, events can occur over the continuous time interval. X = # of events is Bin(n,p) X = # of events is Poisson( )

33. Example The number of Hurricanes over a period of a year in the Caribbean is known to have a Poisson distribution with = 13.1 Determine the probability function of X. Compute the probability that X is at most 8. Compute the probability that X is at least 10. Given that at least 10 hurricanes occur, what is the probability that X is at most 15?

34. Solution X will have a Poisson distribution with parameter = 13.1, i.e.

35. Table of p(x)

37. The Geometric distribution Suppose a Bernoulli trial (S,F) is repeated until a success occurs. Let X = the trial on which the first success (S) occurs. Find the probability distribution of X. Note: the possible values of X are {1, 2, 3, 4, 5, … } The sample space for the experiment (repeating a Bernoulli trial until a success occurs is: S = {S, FS, FFS, FFFS, FFFFS, …, FFF…FFFS, …} p(x) =P[X = x] = P[{FFF…FFFS}] = (1 – p) x – 1 p (x – 1) F’s

38. Thus the probability function of X is: P[X = x] = p(x) = p(1 – p) x – 1 = pq x – 1 A random variable X that has this distribution is said to have the Geometric distribution. Reason p(1) = p, p(2) = pq, p(3) = pq 2, p(4) = pq 3, … forms a geometric series

39. The Negative Binomial distribution Suppose a Bernoulli trial (S,F) is repeated until k successes occur. Let X = the trial on which the k th success (S) occurs. Find the probability distribution of X. Note: the possible values of X are {k, k + 1, k + 2, k + 3, 4, 5, … } The sample space for the experiment (repeating a Bernoulli trial until k successes occurs) consists of sequences of S’s and F’s having the following properties: 1.each sequence will contain k S’s 2.The last outcome in the sequence will be an S.

40. SFSFSFFFFS FFFSF … FFFFFFS A sequence of length x containing exactly k S’s The last outcome is an S The # of S’s in the first x – 1 trials is k – 1. The # of ways of choosing from the first x – 1 trials, the positions for the first k – 1 S’s. The probability of a sequence containing k S’s and x – k F’s.

41. The Hypergeometric distribution Suppose we have a population containing N objects. Suppose the elements of the population are partitioned into two groups. Let a = the number of elements in group A and let b = the number of elements in the other group (group B). Note N = a + b. Now suppose that n elements are selected from the population at random. Let X denote the elements from group A. (n – X will be the number of elements from group B.) Find the probability distribution of X.\

42. Population Group A (a elements) GroupB (b elements) sample (n elements) x N - x

43. Thus the probability function of X is: A random variable X that has this distribution is said to have the Hypergeometric distribution. The total number of ways n elements can be chosen from N = a + b elements The number of ways n - x elements can be chosen Group B. The number of ways x elements can be chosen Group A. The possible values of X are integer values that range from max(0,n – b) to min(n,a)

44. The Bernoulli distribution Discrete distributions

45. The Binomial distribution p(x)p(x) x X = the number of successes in n repetitions of a Bernoulli trial p = the probability of success

46. The Poisson distribution Events are occurring randomly and uniformly in time. X = the number of events occuring in a fixed period of time.

47. The Geometric Distribution The Negative Binomial Distribution The Binomial distribution, the Geometric distribution and the Negative Binomial distribution each arise when repeating independently Bernoulli trials The Binomial distribution the Bernoulli trials are repeated independently a fixed number of times n and X = the numbers of successes The Negative Binomial distribution the Bernoulli trials are repeated independently until a fixed number, k, of successes has occurred and X = the trial on which the k th success occurred. The Geometric distribution the Bernoulli trials are repeated independently the first success occurs (,k = 1) and X = the trial on which the 1 st success occurred.

48. The Geometric distribution Suppose a Bernoulli trial (S,F) is repeated until a success occurs. Let X = the trial on which the first success (S) occurs. Find the probability distribution of X. Note: the possible values of X are {1, 2, 3, 4, 5, … } The sample space for the experiment (repeating a Bernoulli trial until a success occurs is: S = {S, FS, FFS, FFFS, FFFFS, …, FFF…FFFS, …} p(x) =P[X = x] = P[{FFF…FFFS}] = (1 – p) x – 1 p (x – 1) F’s

49. Thus the probability function of X is: P[X = x] = p(x) = p(1 – p) x – 1 = pq x – 1 A random variable X that has this distribution is said to have the Geometric distribution. Reason p(1) = p, p(2) = pq, p(3) = pq 2, p(4) = pq 3, … forms a geometric series

50. Example Suppose a die is rolled until a six occurs Success = S = {six}, p = 1 / 6. Failure = F = {no six} q = 1 – p = 5 / 6. 1.What is the probability that it took at most 5 rolls of a die to roll a six? 2.What is the probability that it took at least 10 rolls of a die to roll a six? 3.What is the probability that the “first six” occurred on an even number toss? 4.What is the probability that the “first six” occurred on a toss divisible by 3 given that the “first six” occurred on an even number toss?

51. Solution Let X denote the toss on which the first head occurs. Then X has a geometric distribution with p = 1 / 6.. q = 1 – p = 5 / 6. 1. P[X ≤ 5]? 2. P[X ≥ 10]? 3. P[X is divisible by 2]? 4. P[X is divisible by 3| X is divisible by 2]?

52. 1. P[X ≤ 5]? Using Note also

53. 2. P[X ≥ 10]? Using

54. 3. P[X is divisible by 2]?

55. 4. P[X is divisible by 3| X is divisible by 2]?

56. Hence

57. The Negative Binomial distribution Suppose a Bernoulli trial (S,F) is repeated until k successes occur. Let X = the trial on which the k th success (S) occurs. Find the probability distribution of X. Note: the possible values of X are {k, k + 1, k + 2, k + 3, 4, 5, … } The sample space for the experiment (repeating a Bernoulli trial until k successes occurs) consists of sequences of S’s and F’s having the following properties: 1.each sequence will contain k S’s 2.The last outcome in the sequence will be an S.

58. SFSFSFFFFS FFFSF … FFFFFFS A sequence of length x containing exactly k S’s The last outcome is an S The # of S’s in the first x – 1 trials is k – 1. The # of ways of choosing from the first x – 1 trials, the positions for the first k – 1 S’s. The probability of a sequence containing k S’s and x – k F’s.

59. Example Suppose the chance of winning any prize in a lottery is 3%. Suppose that I play the lottery until I have won k = 5 times. Let X denote the number of times that I play the lottery. Find the probability function, p(x), of X

60. Graph of p(x)

61. The Hypergeometric distribution Suppose we have a population containing N objects. Suppose the elements of the population are partitioned into two groups. Let a = the number of elements in group A and let b = the number of elements in the other group (group B). Note N = a + b. Now suppose that n elements are selected from the population at random. Let X denote the elements from group A. (n – X will be the number of elements from group B.) Find the probability distribution of X.\

62. Population Group A (a elements) GroupB (b elements) sample (n elements) x n - x

63. Thus the probability function of X is: A random variable X that has this distribution is said to have the Hypergeometric distribution. The total number of ways n elements can be chosen from N = a + b elements The number of ways n - x elements can be chosen Group B. The number of ways x elements can be chosen Group A. The possible values of X are integer values that range from max(0,n – b) to min(n,a)

64. Suppose that N (unknown) is the size of a wildlife population. To estimate N, T animals are caught, tagged and replaced in the population. (T is known) A second sample of n animals are caught and the number, t, of tagged animals is noted. (n is known and t is the observation that will be used to estimate N). Example: Estimating the size of a wildlife population

65. Note The observation, t, will have a hypergeometric distribution

66. To determine when is maximized compute and determine when the ratio is greater than 1 and less than 1.

67. Now

68. if or and

69. henceif andif alsoif

70. Thus

71. Example: Hyper-geometric distribution Suppose that N = 10 automobiles have just come off the production line. Also assume that a = 3 are defective (have serious defects). Thus b = 7 are defect-free. A sample of n = 4 are selected and tested to see if they are defective. Let X = the number in the sample that are defective. Find the probability function of X. From the above discussion X will have a hyper- geometric distribution i.e.

72. Table and Graph of p(x)

73. Sampling with and without replacement Suppose we have a population containing N objects. Suppose the elements of the population are partitioned into two groups. Let a = the number of elements in group A and let b = the number of elements in the other group (group B). Note N = a + b. Now suppose that n elements are selected from the population at random. Let X denote the elements from group A. (n – X will be the number of elements from group B.) Find the probability distribution of X. 1.If the sampling was done with replacement. 2.If the sampling was done without replacement

74. Solution: 1.If the sampling was done with replacement. Then the distribution of X is the Binomial distn.

75. 2.If the sampling was done without replacement. Then the distribution of X is the hyper-geometric distn.

76. Note:

77. for large values of N, a and b Thus Thus for large values of N, a and b sampling with replacement is equivalent to sampling without replacement.

78. Summary Discrete distributions

79. The Bernoulli distribution Discrete distributions

80. The Binomial distribution p(x)p(x) x X = the number of successes in n repetitions of a Bernoulli trial p = the probability of success

81. The Poisson distribution Events are occurring randomly and uniformly in time. X = the number of events occuring in a fixed period of time.

82. The Negative Binomial distribution the Bernoulli trials are repeated independently until a fixed number, k, of successes has occurred and X = the trial on which the k th success occurred. The Geometric distribution the Bernoulli trials are repeated independently the first success occurs (,k = 1) and X = the trial on which the 1 st success occurred. P[X = x] = p(x) = p(1 – p) x – 1 = pq x – 1 Geometric ≡ Negative Binomial with k = 1

83. The Hypergeometric distribution Suppose we have a population containing N objects. The population are partitioned into two groups. a = the number of elements in group A b = the number of elements in the other group (group B). Note N = a + b. n elements are selected from the population at random. X = the elements from group A. (n – X will be the number of elements from group B.)