EC255 Lecture Notes - Lecture 5: Random Variable, Exponential Distribution, Probability Distribution
Document Summary
Random variable: contains the outcomes of a chance experiment. Discrete random variable: if the set of all possible values is at most a finite or a countable infinite number of possible values. Continuous random variables: take on values at ever point over a given interval. Things that are measured, not counted for (weight, lengths) 2 types of distribution: discrete: constructed from discrete random variables continuous: based on continuous random variables. Discrete distribution: binomial distribution, poisson distribution, hypergeometric distribution. Do not need to used class midpoint (m), use the discrete experiments outcomes (x) Mean or expected value: the long-run average of occurrences binomial: only 2 possible outcomes, success or failure. In any binomial distribution the largest x value that can occur is n and the smallest is 0 to determine sample size using binomial distribution without replacement: Poisson distribution: focuses only on the number of discrete occurrences over some interval or continuum. Does not have a given # of trials.
