'Conditional distributions' presentation slideshows

Chapter 10 Analyzing the Association Between Categorical Variables

1.22k views • 90 slides

Discrete Multivariate Analysis

Discrete Multivariate Analysis. Analysis of Multivariate Categorical Data. Example 1. In this study we examine n = 1237 individuals measuring X , Systolic Blood Pressure and Y, Serum Cholesterol. Example 2.

954 views • 75 slides

Probabilistic Inference

Probabilistic Inference. Agenda. Random variables Bayes rule Intro to Bayesian Networks. Cheat Sheet: Probability Distributions. Event: boolean propositions that may or may not be true in a state of the world For any event x: P(x) ?0 ( axiom ) P(x) =1-P(?x) For any events x,y :

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Did you sign up for My Stat Lab?

Did you sign up for My Stat Lab?. Yes No. Announcements. Homework #1 due Sunday at 10:00 pm Quiz #1 in class August 28 th Part 1 of the Data Project due September 4 th. Data Project. Objective: Ask a question and try to answer it using statistics.

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Learning in Undirected Graphical Models

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Probabilistic Reasoning

Probabilistic Reasoning. (Mostly using Bayesian Networks). Introduction: Why probabilistic reasoning?. The world is not deterministic. (Usually because information is limited.) Ways of coping with uncertainty include default logic, fuzzy logic, and probability theory.

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M&Ms Two-way Tables

M&Ms Two-way Tables. Ellen Gundlach STAT 301 Course Coordinator Purdue University. M&Ms Color Distribution % according to their website. Skittles Color Distribution % according to their hotline. My M&Ms data in counts. My M&Ms data: joint % (divide counts by total = 76).

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Conditional Chow-Liu Tree Structures for Modeling Discrete-Valued Vector Time Series

Conditional Chow-Liu Tree Structures for Modeling Discrete-Valued Vector Time Series. Sergey Kirshner, UC Irvine Padhraic Smyth, UC Irvine Andrew Robertson, IRI. July 10, 2004. Overview. Data and its modeling aspects Model description General Approach Hidden Markov models

491 views • 31 slides

Chapter 1: Exploring Data

Chapter 1: Exploring Data. Section 1.1 Analyzing Categorical Data. The Practice of Statistics, 4 th edition - For AP* STARNES, YATES, MOORE. Chapter 1 Exploring Data. Introduction : Data Analysis: Making Sense of Data 1.1 Analyzing Categorical Data

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Chapter 1: Exploring Data

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STA220: Practice of Statistics 1 Section L0301: Health & Life Sciences September 17, 2013

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Sample Midterm question.

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Chapter 1: Exploring Data

Chapter 1: Exploring Data. Section 1.1 Analyzing Categorical Data. The Practice of Statistics, 4 th edition - For AP* STARNES, YATES, MOORE. Chapter 1 Exploring Data. Introduction : Data Analysis: Making Sense of Data 1.1 Analyzing Categorical Data

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Unsupervised Learning of Visual Taxonomies

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Chapter 1: Exploring Data

Chapter 1: Exploring Data. Section 1.1 Analyzing Categorical Data. The Practice of Statistics, 4 th edition - For AP* STARNES, YATES, MOORE. Chapter 1 Exploring Data. Introduction : Data Analysis: Making Sense of Data 1.1 Analyzing Categorical Data

362 views • 15 slides

Chapter 1: Exploring Data

Chapter 1: Exploring Data. Section 1.1 Analyzing Categorical Data. The Practice of Statistics, 4 th edition - For AP* STARNES, YATES, MOORE. Chapter 1 Exploring Data. Introduction : Data Analysis: Making Sense of Data 1.1 Analyzing Categorical Data

355 views • 15 slides

Penny Stacking

Penny Stacking. By Ricky Benner and Stefanie Richman. Procedure for Penny Stacking. Subjects had to stack as many pennies in piles of 10 as they could in 60 seconds, but they could only stack them on the table one at a time

327 views • 11 slides

Chapter 1: Exploring Data

Chapter 1: Exploring Data. Section 1.1 Analyzing Categorical Data. The Practice of Statistics, 4 th edition - For AP* STARNES, YATES, MOORE. Analyzing Categorical Data. Two-Way Tables and Marginal Distributions

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Chapter Four Day Four

Chapter Four Day Four. Marginal Distributions. Homework. P. 298 23,24,25. Marginal Distributions. Analyzes distribution of each variable separately Marginal dist. = total of subgroup/ total in table. Section 4-2 Relationships in Categorical Data. Conditional Distributions.

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Weight & Height for Female UL Students

f(Y). s Y = 40. Marginal distribution of Y. M Y = 142. Weight Y. s Y|X = 36. Conditional distribution of Y|X = 68. x 1 = 60. x 2 = 64. x 3 = 68. Regression line. Under usual assumptions, the conditional distributions of Y at each level of Height, X, all have the same variance.

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