QTM 100 Lecture Notes - Lecture 6: Random Variable, Normal Distribution, Standard Score

• School: Emory University
• Department: Quantitative Theory & Methods
• Course: QTM 100
• Professor: Ho Kim

Document Summary

If either the multiplication rule or conditional probability does not hold true, then the two events are dependent: probability is key to statistical inference. Probability distribution and random variables: the random variables can be explained by a probability distribution, random variables can be described as either discrete or continuous, a discrete random variable takes on a set of separate values. This probability distribution is viewed in a table: a continuous random variable takes on values that are in an interval. If a random variable has a normal distribution but it is not standard normal (it has some . 1, we can still make inferences on the random variable by standardizing calculations with a z-score.