Poisson Distribution Model - Case Study Example

The customer coming into a store, the average number of the customer to walk in Starbuck within a period of one hour is 32. This is however based on the average as the first hour, one customer may enter while the second hour 8 customer may enter and the value may continue to vary with passing each hour. Therefore, the Poisson distribution model helps us to get an estimate of the customer will enter within the given hours. The data will be given by the chance of an event happening, multiplied by the average raised to the power of X, multiplied by the natural number; e raised in the –ve of the average power which is again divided by the factorial of X.

Kevin is one of the team members who arguably have a taste for shopping. In this case, we are going to monitor the patterns of her online shopping using eBay as the study area. The information obtained from his online shopping habits will be used to project an analysis that will illustrate when likely Kevin will go shopping online. Averagely, the time Kevin goes for shopping online is 2. 6. This includes the average of the time when he would go for a long hour and those he goes for just a short time.

Having this data, it can be easy for us to determine the likely hood that he will go in the 2, 3, 4 or 5 hours. Jack is fond of using the word “right” whenever he wants to drive his point home. I think it also gives him the confidence and the power of conviction whenever he is having a chat with the rest of the members of the class. Using the Poisson distribution model, we can estimate the likelihood that he is going to use the word “right” in his conversation. As from the previous examples, In the case of Starbuck Hotel, the data can be of importance in determining the number of beverages and snacks to prepare.

From the observations, the clients are more likely to come in the afternoon and evening hours.