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b.lect13 - Outline Introduction The Joint Probability Mass...

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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
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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
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Outline Introduction The Joint Probability Mass Function Multivariate Variables Michael Akritas Lecture 13 Chapter 4: Multivariate Variables and Their Distribu
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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
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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. Y = age of a tree, X = the tree’s diameter at breast height. Michael Akritas Lecture 13 Chapter 4: Multivariate Variables and Their Distribu
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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. Y = age of a tree, X = the tree’s diameter at breast height. 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
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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. Y = age of a tree, X = the tree’s diameter at breast height. 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. Y = propagation of an ultrasonic wave, X = tensile strength of substance. Michael Akritas Lecture 13 Chapter 4: Multivariate Variables and Their Distribu
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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. Y = age of a tree, X = the tree’s diameter at breast height. 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. Y = propagation of an ultrasonic wave, X = tensile strength of substance.
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