STA 2023 Lecture Notes - Lecture 11: Busy Signal, Probability Distribution, Standard Deviation
Document Summary
Random variable represents a numerical value associated with each outcome of a probability experiment, denoted by x. X=number of sales calls a salesperson makes in one day. X=hours spent on sales calls in one day. Discrete random variable has a countable number of possible outcomes. Continuous random variable has infinite number of possible outcomes, represented by an interval on the number line. Example 1- discrete or continuous random variable: the number of a"s earned in a section of statistics with 15 students enrolled, the number of cars that travel through a mcdonald"s drive-through in the. Discrete: the speed of the next car that passes a state trooper. The probability distribution of a discrete random variable x provides the possible values of the random variable and their corresponding probabilities. Discrete probability distribution rules: the sum of all the assigned and probabilities must be 1, the probabilities distribution has a probability assigned to each distinct value of the random variable.
