MGSC 2301 Lecture Notes - Lecture 5: Standard Deviation, Random Variable, Probability Distribution
16 Dec 2016
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Document Summary
Binomial x = # of success p(x|p,n)= n! Px (1-p)n-x p = probability of successes on trial x! (n-x) Random variable: a numerical description of the outcome of an experiment. Discrete random variable: a random variable that may assume either a finite number of values or an infinite sequence of values. Continuous random variable: a random variable that may assume any numerical value in an interval or collection of intervals. Probability distribution: a description of how the probabilities are distributed over the values of the random variable. Probability function: a function, denoted by f (x), that provides the probability that x assumes a particular value for a discrete random variable. Discrete uniform probability distribution: a probability distribution for which each possible value of the random variable has the same probability. Expected value: a measure of the central location of a random variable. Variance: a measure of the variability, or dispersion, of a random variable.
