MIS 446 Lecture Notes - Lecture 7: Binomial Distribution, Continuous Or Discrete Variable, Fair Coin

• School: Iowa State University
• Department: Management Information Systems
• Course: MIS 446
• Professor: Jiang Zhengrui

Document Summary

Mis 446 advanced business analytics: probability and sampling distributions. Agenda: probability and probability distributions, sampling distributions, confidence interval estimation, sample size determination. Events: each possible outcome of a variable is an event. An event described by a single characteristic e. g. , a day in january from all days in 2013. An event described by two or more characteristics e. g. a day in january that is also a wednesday from all days in 2013. Complement of an event a (denoted a") All events that are not part of event a e. g. , all days from 2013 that are not in january. Probability examples: suppose we roll 2 dice. Probability die rolls sum to three = 2/36: suppose two consumers try a new product. 4. dislike, dislike: probability at least one dislikes product = 3/4. General addition rule o: if a and b are mutually exclusive, then p(a and b) = 0, so the rule can be simplified: o o.