BUSS1020 Lecture Notes - Lecture 5: Hypergeometric Distribution, Poisson Distribution, Probability Distribution

• School: University of Sydney
• Department: Business Studies
• Course: BUSS1020
• Professor: Erick Li

Document Summary

Buss1020 lecture 5 notes (cid:858)dis(cid:272)rete pro(cid:271)a(cid:271)ilit(cid:455) distri(cid:271)utio(cid:374)s binomial, poisson, hypergeometric(cid:859) Often discrete probabilities rely on being able to count all the events possible. Recall a priori probability: p(event) = number of ways the event can occur / total number of outcomes. When there are many possible outcomes, this can become tricky and time consuimg. Rules for counting the number of possible discrete or catergory outcomes: counting rule 1, if any one of k events can occur on each of n trials, the number of possible outcomes is equal to k^n. Example: if you roll a die 3 times, then there are 6^3 = 216. Example: a customer can either purchase or not purchase a product a, and then spend < or > in total. This gives 2*2 = 4 possible outocmes: counting rule 3, the number of ways that n items can be arranged in a unique order, is n! Example: you have 5 business reports to read.