01:960:285 Lecture Notes - Lecture 10: 5,6,7,8, Random Variable, Probability Distribution

• School: Rutgers University
• Department: Statistics and Biostatistic
• Course: 01:960:285
• Professor: Shyam Moondra

Document Summary

01:960:285 - lecture 10 - chapter 4: random variables and probability distributions. Definition: a variable that assumes association between a value and the random results of an experiment. X = random variable x = specific value of x. P(x = x) = p(x) the probability of x equaling a certain number, x. Rules: 0 p(x) 1, for all x every probability of x must be between 0 and 1, p(x) = 1, for all x the summation of all probabilities of x must equal 1. Example: flip a coin 10 times, then count the number of heads. Let x = the number of heads in 10 trials. Two types of random variables: discrete random variables: The number of people waiting in line is finite. Let x = number of people waiting x can equal 0, 1, 2, , 50, or 51, etc. Discrete probability distributions include: binomial, hypergeometric, and poisson (pronounced pwa-sahn : continuous random variables: