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Unformatted text preview: Outline Introduction The Joint Probability Mass Function Lecture 13 Chapter 4: Multivariate Variables and Their Distribution Michael Akritas Michael Akritas Lecture 13 Chapter 4: Multivariate Variables and Their Distribu Outline Introduction The Joint Probability Mass Function Introduction The Joint Probability Mass Function Definition and Example Marginal Probability Mass Functions Conditional Probability Mass Functions Independence Michael Akritas Lecture 13 Chapter 4: Multivariate Variables and Their Distribu Outline Introduction The Joint Probability Mass Function Multivariate Variables Michael Akritas Lecture 13 Chapter 4: Multivariate Variables and Their Distribu Outline Introduction The Joint Probability Mass Function Multivariate Variables When we record more than one characteristic from each population unit, the outcome variable is multivariate. Michael Akritas Lecture 13 Chapter 4: Multivariate Variables and Their Distribu Outline Introduction The Joint Probability Mass Function Multivariate Variables When we record more than one characteristic from each population unit, the outcome variable is multivariate. I Y = age of a tree, X = the trees diameter at breast height. Michael Akritas Lecture 13 Chapter 4: Multivariate Variables and Their Distribu Outline Introduction The Joint Probability Mass Function Multivariate Variables When we record more than one characteristic from each population unit, the outcome variable is multivariate. I Y = age of a tree, X = the trees diameter at breast height. I Y takes the value 0, or 1 if a child survives a car accident, or not. X takes the value 0, 1, or 2, if the child uses no seat belt, adult seat belt, or child seat. Michael Akritas Lecture 13 Chapter 4: Multivariate Variables and Their Distribu Outline Introduction The Joint Probability Mass Function Multivariate Variables When we record more than one characteristic from each population unit, the outcome variable is multivariate. I Y = age of a tree, X = the trees diameter at breast height. I Y takes the value 0, or 1 if a child survives a car accident, or not. X takes the value 0, 1, or 2, if the child uses no seat belt, adult seat belt, or child seat. I Y = propagation of an ultrasonic wave, X = tensile strength of substance. Michael Akritas Lecture 13 Chapter 4: Multivariate Variables and Their Distribu Outline Introduction The Joint Probability Mass Function Multivariate Variables When we record more than one characteristic from each population unit, the outcome variable is multivariate. I Y = age of a tree, X = the trees diameter at breast height. I Y takes the value 0, or 1 if a child survives a car accident, or not. X takes the value 0, 1, or 2, if the child uses no seat belt, adult seat belt, or child seat....
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This note was uploaded on 03/19/2009 for the course STAT 401 taught by Professor Akritas during the Spring '00 term at Pennsylvania State University, University Park.
 Spring '00
 Akritas
 Probability

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