QMS 102 Lecture 8: Binomial and Poisson Probability

• School: Ryerson University
• Department: Quantitative Methods
• Course: QMS 102
• Professor: Nursel Ruzgar

Document Summary

Qms 102 lecture 8: binomial & poisson probabilities. Random variable represents a possible numerical value from an uncertain event. Discrete random variables produce outcomes that come from a counting process. E. g. number of classes you are taking. Can only assume a countable number of values. A probability distribution for a discrete random variable is mutually exclusive listing of all possible numerical outcomes for that variable & a probability of occurrence associated with each outcome. Continuous random variables produce outcomes that come from a measurement. E. g. your annual salary, or your weight. 15 tosses of a coin; 10 light bulbs taken from a warehouse. Each observation is categorized as to whether or not the event of interest occurred. E. g. head or tail in each toss of a coin; defective or not defective light bulb. Since these 2 categories are mutually exclusive & collectively exhaustive.