# Chapter 3: DISCRETE RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS

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1 Chapter 3: DISCRETE RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS Part 4: Geometric Distribution Negative Binomial Distribution Hypergeometric Distribution Sections 3-7, 3-8 The remaining discrete random variables we ll discuss: Geometric Negative Binomial Hypergeometric Poisson (not on midterm exam 1) 1

2 Geometric Distribution Consider a sequence of independent Bernoulli trials with a success denoted by S and failure denoted by F with P (S) = p and P (F ) = 1 p. Let X = the number of trials until (and including) the first success. Then, X follows a Geometric Distribution. Comparison with binomial: For a random variable with a binomial distribution, we know how many trials we will have, we have n trials. For a random variable with a geometric distribution, we do not know how many trials we will have. It keeps going until we get a success. 2

3 NOTATION: If X follows a geometric distribution with parameter p (for probability of success), we can write X Geo(p) for X = 1, 2, 3,... Example: Let X be a geometric random variable with p = What is the probability that X = 4 (that the first success occurs on the 4th trial)? ANS: For X to be equal to 4, we had 3 failures, and then a success. P (X = 4) = 3

4 Geometric Distribution In a series of Bernoulli trials (independent trials with constant probability p of success), let the random variable X denote the number of trials until the first success. Then, X is a geometric random variable with parameter p such that 0 < p < 1 and the probability mass function of X is f(x) = (1 p) x 1 p for x = 1, 2, 3,... The geometric distribution places mass at the counting numbers {1, 2, 3,...}. Though X can be any positive integer, the mass at the righttailed numbers eventually gets quite small. 4

5 What does the distribution look like for X Geo(p)? f(x) x p=0.8 p=0.5 p=0.2 Is f(x) a legitimate probability mass function? (1 p) x 1 p = p+(1 p)p+(1 p) 2 p+?= 1 x=1 Mathematically this works out because x=0 a x = 1 1 a 5 for 0 < a < 1

6 Mean and Variance of Geometric Distribution If X is a geometric random variable with parameter p, then µ = E(X) = 1 p σ 2 = V (X) = 1 p p 2 Example: Weld strength A test of weld strength involves loading welded joints until a fracture occurs. For a certain type of weld, 80% of the fractures occur in the weld itself, while the other 20% occur in the beam. A number of welds are tested and the tests are independent. 6

7 Let X be the number of tests up to and including the first test that results in a beam fracture. 1. Find P (X 3) {i.e. that the first beam fracture happens on the third trial or later.} ANS: Either a weld fracture or a beam fracture will occur on each Bernoulli trial. We ll call a success a beam fracture. X is a geometric random variable with p = P (X 3) = 1 P (X < 3) {complement} = 1 [P (X = 1)+P (X = 2)] = 1 [ (0.80)(0.20)] =

8 2. Find E(X) and V (X) and the standard deviation of X. ANS: E(X) = 1 p = = 5 σ 2 = V (X) = 1 p p 2 = 0.8 (0.2) 2 = 20 σ = V (X) =

9 Negative Binomial Distribution This distribution is similar to the geometric distribution, but now we re interested in continuing the Bernoulli trials until r successes have been found (you must specify r). Example: Continuing the weld strength example 1. Find the probability that the 3rd beam fracture (success) occurs on the 6th trial. ANS: Recall, P (success) = P (beam fracture) = 0.2 We want the probability that there were 2 successes within the first 5 trials, and the 6th trial WAS a success. 9

10 [( ) ] 5 probability= (0.2) 2 2 (0.8) 3 ( ) 5 = (0.8) 2 3 (0.2) 3 (0.2) =10 (0.8) 3 (0.2) 3 =

11 Negative Binomial Distribution In a series of Bernoulli trials (independent trials with constant probability p of success), let the random variable X denote the number of trials until r successes occur. Then, X is a negative binomial random variable with parameters 0 < p < 1 and r = 1, 2, 3,... and the probability mass function of X is ( ) x 1 f(x) = (1 p) r 1 x r p r for x = r, r + 1, r + 2,... Note that at least r trials are needed to get r successes. 11

12 The visual of the negative binomial distribution (given p and r): 12

13 Mean and Variance of Negative Binomial Distribution If X is a negative binomial random variable with parameters p and r, then µ = E(X) = r/p σ 2 = V (X) = r(1 p)/p 2 Example: Continuing the weld strength example Let X represent the number of trials until 3 beam fractures occur. Then, X follows a negative binomial distribution with parameters p = 0.2 and r = 3. When will the 3rd beam fracture occur? 13

14 f(x) X 1. Find P (X = 4). ANS: 2. Find the expected value of X and the variance of X. ANS: E(X) = =15 σ 2 = V (X) = 3(0.8) (0.2) 2=60 σ =

15 Hypergeometric Distribution The hypergeometric distribution has many applications in finite population sampling. Drawing from a relatively small population without replacement Removing one object affects the next probability (in contrast, sequential probabilities for the binomial which had independent draws remained constant, this is different) Sequential draws are not independent, they depend on what occurred in earlier draws 15

16 Example: Acceptance sampling Suppose a retailer buys goods in lots and each item in the lot can be either acceptable or defective. N K = Total number of items in the lot = Number of defectives in the lot If N = 25 and K = 6, what is the probability that a sample of size 10 has no defectives? We could apply our counting techniques to calculate this, but here, we ll use the general formula for the hypergeometric probability distribution... 16

17 Hypergeometric Distribution (Sampling without replacement in a small finite population) A set of N objects contains: K objects classified as successes N K objects classified as failure A sample of size n objects is selected randomly (without replacement) from the N objects, where K N and n N. Let the random variable X denote the number of successes in the sample. Then X is a hypergeometric random variable and f(x) = ( K x )( N K n x ( ) N n ) for x = max{0, n (N K)} to min{k, n} 17

18 Example: Continuing the example... Set-up the hypergeometric distribution... Information we re given: There are 25 objects altogether, and 6 are defectives. We also know that we are going to choose or sample 10 objects. We re going to calculate the probability of choosing 0 defects, so we ll label getting a defect as a success (it s just a label). A set of N = 25 objects contains: K = 6 objects classified as successes N K = 19 objects classified as failure And n = 10 (we re choosing 10 objects). 18

19 What is the probability that a sample of size 10 has no defectives? Let X be the number of defectives (or successes) found in a sample of size n=10. X is a hypergeometric random variable. Need to calculate P (X = 0). Use the probability mass function for X (shown below) to calculate this probability: P (X = x) = f(x) = ( 6 x )( x ( ) ) for x {0, 1, 2, 3, 4, 5, 6} 19

20 ( 6 ) ( 19 ) P (X = 0) = f(0) = 0 10 ( ) = You can use the PMF to generate the probability for any x... ( )( ) 6 19 x 10 x P (X = x) = f(x) = ( ) f(x) X Notice that even though you re drawing n = 10 it is impossible to draw any more than 6 successes (there s only 6 altogether). 20

21 POSSIBLE VALUES FOR X if X is a hypergeometric random variable: highest possible = min{k, n} The largest number of successes you can have is either K (the # of successful objects present) or the number of trials (whichever is a smaller number, thus, min{k, n}). lowest possible = max{0, n (N K)} The smallest number of successes you can have depends on the number of failures in the pool and n. If the number of failures is n, then you can have all failures, and X can be 0. But if not (say there s only a few failures), the smallest possible number of successes is greater than 0 and is n (N K) = n (largest possible number of failures) = n + K N. 21

22 Continuing the example... N = 25 K = 6 N K = 19 n = 10 Minimum possible value of X? max{0, n (N K)} = max{0, 10 19} = max{0, 9} Maximum possible value of X? min{k, n} = min{6, 10} For the hypergeometric, you want to be able to recognize that you have a finite pool you re drawing from without replacement (i.e. not independent sequential draws), and be able to compute the probabilities using the probability mass function. 22

23 Mean and Variance of Hypergeometric Distribution If X is a negative hypergeometric random variable with parameters N, K and n, then µ = E(X) = np σ 2 = V (X) = np (1 p) ( ) N n N 1 where p = K/N. Example: Continuing the acceptance sampling Find the E(X) and V (X). 23

24 ANS: n = 10, N = 25, K = 6, p = K/N = 6/25 = 0.24 E(X) = 10 (6/25) = 2.4 σ 2 = V (X) = 10 (6/25) (19/25) (15/24) =

25 Section 3-9: The Poisson distribution will not be on midterm exam 1 (it will be on midterm exam 2) 25

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