BUAD 310g Chapter 6: Textbook Chapter 6: Discrete Probability Distributions

• School: USC
• Department: Business Administration
• Course: BUAD 310g
• Professor: Mukherjee Gourab

Document Summary

Must be reasonably realistic yet simple enough to be analyzed. Type of random variable (there are 2 types) Pdf and cdf defined either by a list of x-values and probabilities or by mathematical. Probability model- assigns probability to each outcome in the sample space defined by a random process. Random variable- function/rule that assigns a numerical value to each outcome in sample space of a random experiment. Discrete random variable- a countable number of distinct values. Discrete probability distribution- assigns a probability to each value of a discrete random variable x. Sample space (random variable x) set of real numbers (probability distribution, Distribution can be described either by its pdf or cdf equations. (discrete) shows the # of probability of each x-value. Pdf values of x will sum to 1. Random variables and their distributions are described by their parameters. Expected value- e(x) of a discrete random variable is the sum of all x-values weighted by their respective probabilities.