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Design and Analysis of Experiments

Design and Analysis of Experiments

Design and Analysis of Experiments. Dr. Tai-Yue Wang Department of Industrial and Information Management National Cheng Kung University Tainan, TAIWAN, ROC. An alysis o f Va riance. Dr. Tai-Yue Wang Department of Industrial and Information Management National Cheng Kung University

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1.16k views • 95 slides
A Bayesian ? 2 test for goodness of fit 10/23/09 Multilevel RIT

A Bayesian ? 2 test for goodness of fit 10/23/09 Multilevel RIT

A Bayesian ? 2 test for goodness of fit 10/23/09 Multilevel RIT. Overview. Talk about basic ? 2 test. Review with some examples. Talk about the paper with examples. Basic ? 2 test. y 1. y 2. y 3. y 4. y 5. y n.

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902 views • 72 slides
Linear Models in R

Linear Models in R

Linear Models in R Fish 507 F: Lecture 7 Supplemental Readings Practical Regression and ANOVA using R (Faraway, 2002) Chapters 2,3,6,7,8,10,16 http://cran.r-project.org/doc/contrib/Faraway-PRA.pdf

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1.37k views • 51 slides
Chapter 5 Introduction to Factorial Designs

Chapter 5 Introduction to Factorial Designs

Chapter 5 Introduction to Factorial Designs. 5.1 Basic Definitions and Principles. Study the effects of two or more factors. Factorial designs Crossed: factors are arranged in a factorial design Main effect: the change in response produced by a change in the level of the factor.

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424 views • 41 slides
Model Evaluation and Selection

Model Evaluation and Selection

Model Evaluation and Selection. Example Objective: Demonstrate how to evaluate a single model and how to compare alternative models. Evaluating the Sufficiency of a Single Model (followup to example of Mediation Test). When this model is run, a variety of

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901 views • 17 slides
Structural Equation Modeling 3

Structural Equation Modeling 3

Structural Equation Modeling 3. Psy 524 Andrew Ainsworth. Model Identification. Only identified models can be estimated in SEM A model is said to be identified if there is a unique solution for every estimate Y = 10 Y = a + b

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558 views • 17 slides
Model adequacy checking in the ANOVA

Model adequacy checking in the ANOVA

Model adequacy checking in the ANOVA. Checking assumptions is important Normality Constant variance Independence Have we fit the right model? Later we will talk about what to do if some of these assumptions are violated For more, see section 3.4, pg. 75.

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1.92k views • 10 slides

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