STAT230 Study Guide - Bernoulli Trial, Log-Normal Distribution, Erlang Distribution

• School: University of Waterloo
• Department: Statistics
• Course: STAT230
• Professor: Diana Parry

Document Summary

Y ~ normal (m t-distribution n independent trials (replacement) Y = np, s n p l 0 t. Note: you are only responsible for those distributions that are covered in lecture (not beta or gamma) 1 the continuous uniform and f distributions are not illustrated because they are somewhat unrelated to these distributions. The multinomial, multivariate hypergeometric, bivariate normal, and weibull distributions are not shown and are not covered in lecture. 2 the beta distribution relates to the probability of success in a set of bernoulli trials (e. g. estimating p for an unfair coin). 3 the gamma distribution has a number of special cases, one of which (the erlang distribution) is the waiting time until the nth event in a poisson process. The exponential distribution is the waiting time until the first such event. A bernoulli trial can result in a success with probability p or a failure with probability q = 1 p.