Question

In: Statistics and Probability

Real life applications of Poisson distribution with explain Or The uses of Poisson distribution in real...

Real life applications of Poisson distribution with explain

Or
The uses of Poisson distribution in real life and how?

and Real life applications of Hypergeometric , binomial , geometric distribution with explain
Or
The uses of Hypergeometric, binomial, geometric distribution in real life and how?

Solutions

Expert Solution

(a)

Real life application of Poisson distribution:

Number of accidents at a certain location

Explanation: Probability of accident is extremely small but number of vehicles is quite large. So Poisson Distribution can be applied to estimate the number of accidents in a certain locality

(b)

(i)

Real life application of Hypergeometric distribution:

Consider the situation in a factory where 100 items are produced per day. We supply these parts in boxes of 500 items every week. Thus, lot size = 500. It is known that 2% items are defectives. Suppose sampling inspection of 25 items is done. Then,the number of defective items out of 25 items can be obtained through hypergeometric distribution.Hypergeometric distribution is applicable when items are selected out of a finite lot size with known number of defects and sample items are selected without replacement.

(ii)

Real life application of binomial distribution:

Consider a test consisting of total of 20 question. Each question has got 4 multiple choices of which only 1 choice is correct. If a student randomly selects each answer, we can find out his probability of success in the test using Binomial Distribution.Binomial Distribution is applicable when the total number of trial is fixed, each trial has only two outcomes, the probability of success in each trial is constant and the trials are independent.

(iii)

Real life application of geometric distribution:

Consider the owner of a store wants to know how many customers enter a store until that customer makes a purchase. Geometric distribution is applicable when one is interested in the expected number of failures before the first success.


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