5. Here's an example where the Poisson distribution was used in a maternity hospital to work out how many births would be expected during the night. The hospital had 3000 deliveries each year, so if these happened randomly around the clock 1000 deliveries would be expected between the hours of midnight and 8.00 a.m. This is the time when many staff are off duty, and it is important to ensure that there will be enough people to cope with the workload on any night. The average number of deliveries per night is 1000/365, which is 2.74. From this average rate the probability of delivering 0,1,2, etc babies each night can be calculated using the Poisson distribution. Some probabilities are: P(0) 2.74°e-2.74 / 0! P(1) 2.74' e-2.74 / 1! = 0.177 P(2) 2.742 e-2.74 / 2! = 0.242 P(3) 2.743 e-2.74 / 3! = 0.221 = 0.065 (i) On how many days in the year would 5 or more deliveries be expected in the night? (ii) Over the course of one year, what is the Expected number of deliveries in any DAY?

Recall that Benford's Law claims that numbers chosen from very large data files tend to have "1" as the first nonzero digit disproportionately often. In fact, research has shown that if you randomly draw a number from a very large data file, the probability of getting a number with "1" as the leading digit is about 0.301. Now suppose you are an auditor for a very large corporation. The revenue report involves millions of numbers in a large computer file. Let us say you took a random sample of n = 220 numerical entries from the file and r = 49 of the entries had a first nonzero digit of 1. Let p represent the population proportion of all numbers in the corporate file that have a first nonzero digit of 1. A) What is the value of the sample test statistic? (Round your answer to two decimal places.)B) Find the P-value of the test statistic. (Round your answer to four decimal places.)

Recall that Benford's Law claims that numbers chosen from very large data files tend to have "1" as the first nonzero digit disproportionately often. In fact, research has shown that if you randomly draw a number from a very large data file, the probability of getting a number with "1" as the leading digit is about 0.301. Now suppose you are an auditor for a very large corporation. The revenue report involves millions of numbers in a large computer file. Let us say you took a random sample of n = 220 numerical entries from the file and r = 49 of the entries had a first nonzero digit of 1. Let p represent the population proportion of all numbers in the corporate file that have a first nonzero digit of 1.

Benford's Law claims that numbers chosen from very large data files tend to have "1" as the first nonzero digit disproportionately often. In fact, research has shown that if you randomly draw a number from a very large data file, the probability of getting a number with "1" as the leading digit is about 0.301. Suppose you are an auditor for a very large corporation. The revenue report involves millions of numbers in a large computer file. Let us say you took a random sample of n = 250 numerical entries from the file and r = 60 of the entries had a first nonzero digit of 1. Let p represent the population proportion of all numbers in the corporate file that have a first nonzero digit of 1. Test the claim that p is less than 0.301 by using α = 0.01. What does the area of the sampling distribution corresponding to your P-value look like?         a. The area in the right tail of the standard normal curve.   b. The area not including the right tail of the standard normal curve.…

This cumulative review problem uses material from Chapters 3, 5, and 10. Recall that the Poisson distribution deals with rare events. Death from the kick of a horse is a rare event, even in the Prussian army. The following data are a classic example of a Poisson application to rare events. The data represent the number of deaths from the kick of a horse per army corps per year for 10 Prussian army corps over a period of time. Let x represent the number of deaths and f the frequency of x deaths. x 0 1 2 3 or more f 111 61 24 4 (a) First, we fit the data to a Poisson distribution.The Poission distribution states  P(x) =  e−λλx x! ,  where  λ ≈ x  (sample mean of x values). From our study of weighted averages, we get the following. x =  Σxf Σf Verify that  x = 0.605.  Hint: For the category 3 or more, use 3. x =  (b) Now we have  P(x) =  e−0.605(0.605)x x!  for  x = 0, 1, 2, 3  .  Find  P(0),   P(1),   P(2),  and  P(3 ≤ x).  Round to three places…

Question 4 Multi-Server QueueA supermarket manager is trying to decide how many cashers to employ for the peak time. The service times for check outs are exponentially distributed with a mean service time of 3 minutes. Customer arrivals to cashiers follow a Poisson arrival process with an average 110 customers per hour.a. What is the minimum number of cashers that would be needed to have the utilization less than one?b. If the waiting time is too long, customers might leave the store or even do not enter the store. It is estimated that for every minute of waiting time, the supermarket loses a potential profit of 50 HKD every hour from lost sales. The hourly wage for a casher is 80 HKD / hour. How many cashers should the supermarket employ to maximize its profit? (Keep four decimal places.