# Binomial random variables

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1 Binomial and Poisson Random Variables Solutions STAT-UB.0103 Statistics for Business Control and Regression Models Binomial random variables 1. A certain coin has a 5% of landing heads, and a 75% chance of landing tails. (a) If you flip the coin 4 times, what is the chance of getting exactly heads? Solution: There are 6 outcomes whith exactly heads: HHT T, HT HT, HT T H, T HHT, T HT H, T T HH. By independence, each of these outcomes has probability (.5) (.75). P(exactly heads out of 4 flips) = 6(.5) (.75). (b) If you flip the coin 10 times, what is the chance of getting exactly heads? Solution: Rather than list all outcomes, we will use a counting rule. There are ( ) 10 ways of choosing the positions for the two heads; each of these outcomes has probability (.5) (.75) 8. ( ) 10 P(exactly heads out of 10 flips) = (.5) (.75) 8.. Suppose that you are rolling a die eight times. Find the probability that the face with two spots comes up exactly twice. Solution: Let X be the number of times that we get the face with two spots. This is a binomial random variable with n = 8 and p = 1 6. We compute ( ) n P (X = ) = p (1 p) n = ) ( 1 6 ( ) ( ) 5 6 6

2 3. The probability is 0.04 that a person reached on a cold call by a telemarketer will make a purchase. If the telemarketer calls 40 people, what is the probability that at least one sale with result? Solution: Let X be the number of sales. This is a binomial random variable with n = 40 and p = P (X 1) = 1 P (X < 1) = 1 P (X = 0) ( ) n = 1 p 0 (1 p) n 0 0 = 1 (0.96) Page

3 4. A new restaurant opening in Greenwich village has a 30% chance of survival during their first year. If 16 restaurants open this year, find the probability that (a) exactly 3 restaurants survive. Solution: Let X be the number that survive. This is a binomial random variable with n = 16 and p = 0.3. Therefore, P (X = 3) = (0.3) 3 (1 0.3) ( 16 3) 3 =.146 (b) fewer than 3 restaurants survive. Solution: P (X < 3) = (0.3) 0 (0.7) =.099. (0.3) 1 (0.7) (0.3) (0.7) 14 (c) more than 3 restaurants survive. Solution: P (X > 3) = 1 P (X 3) = 1 ( ) =.754. Page 3

4 Poisson random variables 5. The number of calls arriving at the Swampside Police Station follows a Poisson distribution with rate 4.6/hour. What is the probability that exactly six calls will come between 8:00 p.m. and 9:00 p.m.? Solution: Let X be the number of calls that arrive between 8:00 p.m. and 9:00 p.m. This is a Poisson random variable with mean λ = E(X) = (4.6 calls/hour)(1 hour) = 4.6 calls. P (X = 6) = λ6 6! e λ = (4.6)6 e ! 6. In the station from Problem 5, find the probability that exactly 7 calls will come between 9:00 p.m. and 10:30 p.m. Solution: Let X be the number of calls that arrive between 9:00 p.m. and 10:30 p.m. This is a Poisson random variable with mean λ = E(X) = (4.6 calls/hour)(1.5 hours) = 6.9 calls. P (X = 7) = λ7 7! e λ = (6.9)7 e ! 7. Car accidents occur at a particular intersection in the city at a rate of about /year. Estimate the probability of no accidents occurring in a 6-month period. Solution: Let X be the number of car accidents. This is Poisson random variable with mean λ = E(X) = ( accidents/year)(0.5 years) = 1 accident. P (X = 0) = λ0 0! e λ = e Page 4

5 8. In the intersection from Problem 7, estimate the probability of two or more accidents occurring in a year. Solution: Let X be the number of car accidents. This is Poisson random variable with mean λ = E(X) = ( accidents/year)(1.0 years) = accident. P (X ) = 1 P (X < ) [ ] λ 0 = 1 0! e λ + λ1 1! e λ.594. Page 5

6 Empirical rule with Binomial and Poisson random variables 9. If X is a Poisson random variable with λ = 5, would it be unusual to get a value of X which is less than 190? Solution: Set µ = E(X) = λ = 55, σ = sd(x) = λ = 15. Define z to be the number of standard deviations above the mean that 190 is, i.e. Then, 190 = µ + σz. z = 190 µ = 35 σ A value of X which is below 190 is more than.33 standard deviations below the mean of X. The empirical rule tells us that observations more than standard deviations away from the mean are unusual (they occur less than 95% of the time). Therefore, values of X below 190 are unusual. 10. The probability is 0.10 that a person reached on a cold call by a telemarketer will make a purchase. If the telemarketer calls 00 people, would it be unusual for them to get 30 purchases? Solution: Let X be the number of purchases. This is a Binomial random variable with size n = 00 and success probability p = the expectation and standard deviation of X are µ = np = (00)(.10) = 0 σ = np(1 p) = (00)(.10)(.90) = Since np 15 and np(1 p) 15, the distribution of X can be approximated by the empirical rule. Using the empirical rule approximation, 95% of the time, X will be in the range µ ± σ, or (11.6, 8.4), and 99.7% of the time, X will be in the range µ ± 3σ, or (7.4, 3.6). We would see X 30 less than 5% of the time. It would be unusual to see X = 30, but not highly unusual. Page 6

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