The Poisson Probability Distribution

The Poisson probability distribution, named after the French mathematician Simeon-Denis. Poisson is another important probability distribution of a discrete random variable that has a large number of applications. Suppose a washing machine in a Laundromat breaks down an average of three times a month. We may want to find the probability of exactly two breakdowns during the next month. This is an example of a Poisson probability distribution problem. Each breakdown is called an occurrence in Poisson probability distribution terminology.The Poisson probability distribution is applied to experiments with random and independent occurrences. The occurrences are random in the sense that they do not follow any pattern, and, hence, they are unpredictable. Independence of occurrences means that one occurrence (or nonoccurrence) of an event does not influence the successive occurrences or nonoccurrences of that event. The occurrences are always considered with respect to an interval. In the ex ample of the washing machine, the interval is one month. The interval may be a time interval, a space interval, or a volume interval.The actual number of occurrences within an interval is random and independent. If the average number of occurrences for a given interval is known, then by using the Poisson probability distribution, we can compute the probability of a certain number of occurrences, x, in that interval. Note that the number of actual occurrences in an interval is denoted by x. The following three conditions must be satisfied to apply the Poisson probability distribution. 1. x is a discrete random variable. 2. The occurrences are random. 3. The occurrences are independent.The following are three examples of discrete random variables for which the occurrences are random and independent. Hence, these are examples to which the Poisson probability distribution can be applied. 1. Consider the number of telemarketing phone calls received by a household during a given day. In t his example, the receiving of a telemarketing phone call by a household is called an occurrence, the interval is one day (an interval of time), and the occurrences are random (that is, there is no specified time for such a phone call to come in) and discrete.The total number of telemarketing phone calls received by a household during a given day may be 0, 1, 2, 3, 4, and so forth. The independence of occurrences in this example means that the telemarketing phone calls are received individually and none of two (or more) of these phone calls are related. 2. Consider the number of defective items in the next 100 items manufactured on a machine. In this case, the interval is a volume interval (100 items).The occurrences (number of defective items) are random and discrete because there may be 0, 1, 2, 3, †¦ , 100 defective items in 100 items. We can assume the occurrence of defective items to be independent of one another. 3. Consider the number of defects in a 5-foot-long iron rod. The interval, in this example, is a space interval (5 feet). The occurrences (defects) are random because there may be any number of defects in a 5-foot iron rod. We can assume that these defects are independent of one another. The Poisson Probability Distribution The Poisson probability distribution, named after the French mathematician Simeon-Denis. Poisson is another important probability distribution of a discrete random variable that has a large number of applications. Suppose a washing machine in a Laundromat breaks down an average of three times a month. We may want to find the probability of exactly two breakdowns during the next month. This is an example of a Poisson probability distribution problem. Each breakdown is called an occurrence in Poisson probability distribution terminology.The Poisson probability distribution is applied to experiments with random and independent occurrences. The occurrences are random in the sense that they do not follow any pattern, and, hence, they are unpredictable. Independence of occurrences means that one occurrence (or nonoccurrence) of an event does not influence the successive occurrences or nonoccurrences of that event. The occurrences are always considered with respect to an interval. In the ex ample of the washing machine, the interval is one month. The interval may be a time interval, a space interval, or a volume interval.The actual number of occurrences within an interval is random and independent. If the average number of occurrences for a given interval is known, then by using the Poisson probability distribution, we can compute the probability of a certain number of occurrences, x, in that interval. Note that the number of actual occurrences in an interval is denoted by x. The following three conditions must be satisfied to apply the Poisson probability distribution. 1. x is a discrete random variable. 2. The occurrences are random. 3. The occurrences are independent.The following are three examples of discrete random variables for which the occurrences are random and independent. Hence, these are examples to which the Poisson probability distribution can be applied. 1. Consider the number of telemarketing phone calls received by a household during a given day. In t his example, the receiving of a telemarketing phone call by a household is called an occurrence, the interval is one day (an interval of time), and the occurrences are random (that is, there is no specified time for such a phone call to come in) and discrete.The total number of telemarketing phone calls received by a household during a given day may be 0, 1, 2, 3, 4, and so forth. The independence of occurrences in this example means that the telemarketing phone calls are received individually and none of two (or more) of these phone calls are related. 2. Consider the number of defective items in the next 100 items manufactured on a machine. In this case, the interval is a volume interval (100 items).The occurrences (number of defective items) are random and discrete because there may be 0, 1, 2, 3, †¦ , 100 defective items in 100 items. We can assume the occurrence of defective items to be independent of one another. 3. Consider the number of defects in a 5-foot-long iron rod. The interval, in this example, is a space interval (5 feet). The occurrences (defects) are random because there may be any number of defects in a 5-foot iron rod. We can assume that these defects are independent of one another.