282 search results for "ANova"

What makes us happy? Lets look at data to find out.

November 13, 2013
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What makes us happy? Lets look at data to find out.

I've had a lot of different jobs over the past 4 years, and I've had some incredible experiences along the way. Lately, I've been struggling with what to do next. Or perhaps more accurately, I've been struggling with how to decide what to do next. Deci...

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A slightly different introduction to R, part V: plotting and simulating linear models

November 11, 2013
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A slightly different introduction to R, part V: plotting and simulating linear models

In the last episode (which was quite some time ago) we looked into comparisons of means with linear models. This time, let’s visualise some linear models with ggplot2, and practice another useful R skill, namely how to simulate data from known models. While doing this, we’ll learn some more about the layered structure of a

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A small comparison of bio-equivalence calculations.

November 10, 2013
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Last week I looked at two-way cross-over studies and followed the example of Schütz (http://bebac.at/) in the analysis. Since the EU has its on opinions (Questions & Answers: Positions on specific questions addressed to the pharmacokinetics working party) and two example data sets, I was wondering how the various computations compared.Data There...

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R intro seminars, take 2: some slides about data frames, linear models and statistical graphics

November 7, 2013
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R intro seminars, take 2: some slides about data frames, linear models and statistical graphics

I am doing a second installment of the lunch seminars about data analysis with R for the members of the Wright lab. It’s pretty much the same material as before — data frames, linear models and some plots with ggplot2 — but I’ve sprinkled in some more exercises during the seminars. I’ve tried emphasising scripting a

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Statistical aspects of two-way cross-over studies

November 3, 2013
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I ran into this presentation on Statistical aspects of two-way cross-over studies by Ing. Helmut Schütz (http://bebac.at). He presented some code and referred to the bear package. The bear package is menu driven, which is not my thing. I had to try and do that in R via other packages. The aim is to estimate if the...

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How Do You Write Your Model Definitions?

October 20, 2013
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How Do You Write Your Model Definitions?

I’m often irritated by that when a statistical method is explained, such as linear regression, it is often characterized by how it can be calculated rather than by what model is assumed and fitted. A typical example of this is that linear regression is often described as a method that uses ordinary least squares to calculate the best...

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Science at the speed of ligth

October 15, 2013
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Science at the speed of ligth

May be is not going that fast, but at the speed of R at least. And R is pretty quick. This has pros and cons. I think that understanding the drawbacks is key to maximize the good things of speed, … Continue reading →

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“Statistical Models with R” Course – Milano, October 24-25, 2013

September 19, 2013
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MilanoR, in collaboration with Quantide, organizes "Statistical Models with R" Course October 24-25, 2013 Course description This two-day course shows a wide variety of statistical models with R ranging from Linear Models (LM) to Generalized Linear Models (GLM) modelling, in … Continue reading →

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informative hypotheses (book review)

September 18, 2013
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informative hypotheses (book review)

The title of this book Informative Hypotheses somehow put me off from the start: the author, Hebert Hoijtink, seems to distinguish between informative and uninformative (deformative? disinformative?) hypotheses. Namely, something like H0: μ1=μ2=μ3=μ4 is “very informative” and the alternative Ha is completely uninformative, while the “alternative null” H1: μ1<μ2=μ3<μ4 is informative. (Hence the < signs on

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Mixed models; Random Coefficients, part 2

September 14, 2013
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Continuing from random coefficients part 1, it is time for the second part. To quote the SAS/STAT manual 'a random coefficients model with error terms that follow a nested structure'. The additional random variable is monthc, which is a factor con...

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