349 search results for "anova"

ANOVA and Tukey’s test on R

June 13, 2013
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ANOVA and Tukey’s test on R

OBS: This is a full translation of a portuguese version. In many different types of experiments, with one or more treatments, one of the most widely used statistical methods is analysis of variance or simply ANOVA . The simplest ANOVA can be called “one way” or “single-classification” and involves the analysis of data sampled from The post ANOVA...

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Serious stats: using multilevel models to get accurate inferences for repeated measures ANOVA

June 13, 2013
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This article from my other blog may be of interest to readers of this blog: http://seriousstats.wordpress.com/2013/04/18/using-multilevel-models-to-get-accurate-inferences-for-repeated-measures-anova-designs/

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Visualizing a One-Way ANOVA using D3.js

May 31, 2013
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A while ago I was playing around with the JavaScript package D3.js, and I began with this visualization—that I never really finished—of how a one-way ANOVA is calculated. I wanted to make the visualization interactive, and I did integrate some interactive elements. For instance, if you hover over a data point it will show the residual, and its value will be highlighted in...

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Analytical and simulation-based power analyses for mixed-design ANOVAs

May 21, 2013
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Analytical and simulation-based power analyses for mixed-design ANOVAs

In this post I show some R-examples on how to perform power analyses for mixed-design ANOVAs. The first example is analytical—and adapted from formulas used in G*Power (Faul et al., 2007), and the second example is a Monte Carlo simulation. Read more

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Analytical and simulation-based power analyses for mixed-design ANOVAs

May 21, 2013
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Analytical and simulation-based power analyses for mixed-design ANOVAs

In this post I show some R-examples on how to perform power analyses for mixed-design ANOVAs. The first example is analytical — adapted from formulas used in G*Power (Faul et al., 2007), and the second example is a Monte Carlo simulation. The source code is embedded at the end of this post. Both functions require a dataframe, containing the parameters that will be used in...

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Simulation shows gain of clmm over ANOVA is small

May 5, 2013
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Simulation shows gain of clmm over ANOVA is small

After last post's setting up for a simulation, it is now time to look how the models compare. To my disappointment with my simple simulations of assessors behavior the gain is minimal. Unfortunately, the simulation took much more time than I ...

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Using multilevel models to get accurate inferences for repeated measures ANOVA designs

April 18, 2013
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Using multilevel models to get accurate inferences for repeated measures ANOVA designs

It is now increasingly common for experimental psychologists (among others) to use multilevel models (also known as linear mixed models) to analyze data that used to be shoe-horned into a repeated measures ANOVA design. Chapter 18 of Serious Stats introduces multilevel models by considering them as an extension of repeated measures ANOVA models that can

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Using multilevel models to get accurate inferences for repeated measures ANOVA designs

April 18, 2013
By
Using multilevel models to get accurate inferences for repeated measures ANOVA designs

It is now increasingly common for experimental psychologists (among others) to use multilevel models (also known as linear mixed models) to analyze data that used to be shoe-horned into a repeated measures ANOVA design. Chapter 18 of Serious Stats introduces multilevel models by considering them as an extension of repeated measures ANOVA models that can

Read more »

Formulae in R: ANOVA and other models, mixed and fixed

January 10, 2013
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R’s formula interface is sweet but sometimes confusing. ANOVA is seldom sweet and almost always confusing. And random (a.k.a. mixed) versus fixed effects decisions seem to hurt peoples’ heads too. So, let’s dive into the intersection of these three. I’m aware that there are lots of packages for running ANOVA models that make things nicer

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N-Way ANOVA

September 15, 2012
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N-Way ANOVA example Two-way analysis of variance is where the rubber hits the road, so to speak. This extends the concepts of ANOVA with only one factor to two factors. When there are two factors this means that there can be an interaction between the two factors that should be tested. As one might expect

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