STAT 2060 Lecture Notes - Lecture 4: Probability Mass Function, Standard Deviation, Natural Number

• School: University of Guelph
• Department: Statistics
• Course: STAT 2060
• Professor: Lorna Deeth

Document Summary

A random variable is a variable that takes on numerical values according to a chance process. These random variables can be discrete or continuous. Example: # of heads when flipping a coin 10 times, height of a randomly selected student. Discrete random variables take on a countable, or countably in nite, number of possible values. That is, for a discrete random variable x, we can (theoretically) count all the possible values x can take on. Example: # of heads when we flip a coin 10 times, # of rolls of a die until i roll a 4. We can represent the values of x in a distribution. Visually, x could be 0, 1, 2, 3,4, 5, 6, 7, 88, 9, 10. In comparison, continuous random variables take on an in nite number of possible values. That is, for a continuous random variable x, it is not possible to count all of the possible values x can take on.