'Random samples' diaporamas de présentation

Group Analysis

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Quasi-Random Techniques

Quasi-Random Techniques. Don P. Mitchell. MC Integration for Rendering. Ray tracing yields point samples f(x i ) Discontinuous f thwarts efficient methods High dimensionality of distributed RT. Truly Random x i. Useful mostly in cryptography Tricky to get uniform distribution

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The Importance of Different Social Networks for Infectious Diseases

The Importance of Different Social Networks for Infectious Diseases. Fredrik Liljeros Stockholm University Karolinska institutet Supported by the Swedish Institute for Public Health and The Swedish Emergency Management Agency. S-GEM. Stockholm Group for Epidemic Modelling , S-GEM.

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The standard error of the sample mean and confidence intervals

The standard error of the sample mean and confidence intervals. How far is the average sample mean from the population mean? In what interval around mu can we expect to find 95% or 99% or sample means. An introduction to random samples.

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Wilcoxon Rank Sum Test

Wilcoxon Rank Sum Test. 1. Wilcoxon with both n 1 and n 2 < 10 2. Wilcoxon with both n 1 and n 2 ? 10 3. Examples. Wilcoxon Rank Sum Test. Recall from last week:

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THE CENTRAL LIMIT THEOREM

THE CENTRAL LIMIT THEOREM. The World is Normal Theorem. Sampling Distribution of x- normally distributed population. n=10. Sampling distribution of x: N ( ? , ? /?10). ?/?10. Population distribution: N( ? , ? ). ?. Normal Populations. Important Fact:

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Module 12: Populations, Samples and Sampling Distributions

Module 12: Populations, Samples and Sampling Distributions. This module provides basic information about the statistical concepts of populations and samples, selecting samples from population and the critical issue of sampling distributions. Reviewed 05 May 05 / MODULE 12.

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Healey Chapter 7 Estimation Procedures

Healey Chapter 7 Estimation Procedures. Using the Sampling Distribution to Construct Confidence Intervals. Outline:. The logic of estimation How to construct and interpret confidence interval estimates for: Sample means Sample Proportions. The Logic Behind Estimation .

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Issues in Interpreting Correlations:

Issues in Interpreting Correlations:. Correlation does not imply causality: X might cause Y. Y might cause X. Z (or a whole set of factors) might cause both X and Y. Factors affecting the size of a correlation coefficient: 1. Sample size and random variation:

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Please get out materials for notes.

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A holiday gift shopping Public Service Announcement

A holiday gift shopping Public Service Announcement. Professor Lee Silvi, Director Fire Science Technology Program Lakeland Community College. Fire Extinguishers Make Great Gifts. Even home fire extinguishers should: Be UL or FM listed. Fire Extinguishers Make Great Gifts.

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Exploring Populations

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CHAPTER 4 Designing Studies

CHAPTER 4 Designing Studies. 4.1 Samples and Surveys. Population, Census, and Sample. The population in a statistical study is the entire group of individuals we want information about. A census collects data from every individual in the population.

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Tips on Critiquing Articles

Tips on Critiquing Articles. Tip #1: Know the goal of Educational Research. The goal of the educational research is to observe phenomena in the field of education and attempt to explain why these phenomena exist. Tips on Critiquing Articles. Tip #2: Know how to Dissect a Journal Article.

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CHAPTER 8: Producing Data: Sampling

CHAPTER 8: Producing Data: Sampling. Lecture PowerPoint Slides. Chapter 8 Concepts. Population vs. Sample How to Sample Badly Simple Random Samples Inference About the Population Other Sampling Designs Cautions About Sample Surveys. Chapter 8 Objectives.

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Chi-Square Test

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Relationship Between Sample Data and Population Values

Relationship Between Sample Data and Population Values.

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Sampling Distributions and Forward Inference

Sampling Distributions and Forward Inference. By simulating the process of drawing random samples of size N from a population with a specific mean and variance, we can learn (a) how much error we can expect on average and

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Sampling Distribution of Means: Basic Theorems

Sampling Distribution of Means: Basic Theorems. is an unbiased estimate of . Consider N samples each consisting of 2 observations sampled at random from a single population with mean and variance . is the i-th

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