PROBABILITY DISTRIBUTION (II)

            According to Doane and Seward (2011) event is any subset of finding in the sample space, and the simple event is a single outcome, in the other hand, the compound event is two or more simple events. The sample consisted of multiple simple events (compound event), each other may be identical or different, in the same sample. With the compound event, we can add the individual probability to obtain any desired event probability. (Doane & Seward, 2011). 

            Conditional probability is the probability of event A given that event B has occurred (Doane & Seward, 2011) we can estimates the probability of further outcomes or events happening, and if the occurrence of one event does not affect the occurrence of the other, this statistics is called Statistical Independence (The University of Auckland, 2011) both conditional probability and statistical independence play with the intersection point between two different events.

            The expected value is a sum of all values of a discrete random variable weighted by their respective probability, is a measure of central tendency (Doane & Seward, 2011) it mean the average from all different probability of outcome.

            The binomial distribution counts the number of successes out of a fixed number of trials, the failure and success occur in any different order; and Poisson distribution counts the number of random events in a fixed space of time (The University of Auckland, 2011) in the Poisson distribution the assumption is that all events are independent, events occurs at a constant average rate per unit time, and events cannot occurs simultaneously.

            The normal distribution probability is considered the most important distribution statistic because of its bond with the central Limit Theorem, which according to The University of Auckland (2011) states that any large sum of independent, identically distributed random variable is approximately Normal. It is defined by two parameter the mean and standard deviation, and always is symmetric. (Doane & Seward, 2011).

Bibliography

Doane, D. P., & Seward, L. E. (2011). Applied Statistics in Business and Economics. New York: McGraw-Hill Irwin.

The University of Auckland. (2011). scribd.com. Retrieved July 1, 2012, from Courses Notes STATS 210 : http://www.scribd.com/doc/54171329/Statistical-Theory