I really\nlove statistics. It doesn’t lie, it shows you the real picture, it removes\nbiases\u2026 when applied properly. So I’m deepening my knowledge in statistics and\ntoday I would like to share with you recent discoveries I’ve made.<\/p>\n\n\n\n
A lot of\nprocesses can be modeled using mathematical and statistical methods and by\nmodeling we can find very interesting insights. Also we can calculate the\nprobability of occurrence of certain event. <\/p>\n\n\n\n
In\nstatistics and probability theory there is a term – Poisson distribution, it is\na discrete probability distribution that expresses the probability of a given\nnumber of events occurring in a fixed interval of time or space if these events\noccur with a known constant rate and independently of the time since the last\nevent. <\/p>\n\n\n\n
Yes, I understand, those words above are little bit complicated, so I’ll show an example.For instance, an individual keeping track of the amount of mail they receive each day may notice that they receive an average number of 4 letters per day. If receiving any particular piece of mail does not affect the arrival times of future pieces of mail, i.e., if pieces of mail from a wide range of sources arrive independently of one another, then a reasonable assumption is that the number of pieces of mail received in a day obeys a Poisson distribution. Other examples that may follow a Poisson include the number of phone calls received by a call center per hour and the number of decay events per second from a radioactive source, goals scored during World Cup, the number of meteorites greater than 1 meter diameter that strike Earth in a year, the number of patients arriving in an emergency room between 10 and 11 pm and so on (Wikipedia).<\/p>\n\n\n\n