QMS 102 Lecture Notes - Lecture 6: Probability Distribution, Random Variable, Weighted Arithmetic Mean

• School: Ryerson University
• Department: Quantitative Methods
• Course: QMS 102
• Professor: Boza Tasic

Document Summary

Numeric variables can either be discrete or continuous. Random variable: a variable whose value is determined by the outcome of a random experiment. Discrete random variables: a randome variable whose values are discrete. Discrete probability distribution: a table or form that shows the probability associated with each possible value of a discrete random variable. How to nd out the # of outcomes. # of variables (head or tails) to the power of # of trials (3 times) 2 x 2 x 2 = 8 outcomes. The expected value (or the mean) of a discrete random variable x with n outcomes is denoted by e(x) and is de ned by. Same as calculating the weighted average where the probabilities are weights. Because all probability equals 1, the bottom part of the equation is 1. Examples that may show up on test: denote x = pro t, 200 - cost = pro t, 200 - 22 = 178.