383 search results for "Anova"

Weighted Effect Coding: Dummy coding when size matters

October 31, 2016
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If your regression model contains a categorical predictor variable, you commonly test the significance of its categories against a preselected reference category. If all categories have (roughly) the same number of observations, you can also ...

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Annotated Facets with ggplot2

October 20, 2016
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Annotated Facets with ggplot2

I was recently asked to do a panel of grouped boxplots of a continuous variable, with each panel representing a categorical grouping variable. This seems easy enough with ggplot2 and the facet_wrap function, but then my collaborator wanted p-values on the graphs! This post is my approach to the problem. First of all, one caveat. I’m a

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Raccoon | Ch. 1 – Introduction to Linear Models with R

October 19, 2016
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Raccoon | Ch. 1 – Introduction to Linear Models with R

This is the first chapter of our new web book, Raccoon - Statistical Models with R: it's an introduction to Linear models, with theoratical explanation and lots of examples + two summary tables with linear model formulae and functions in R The post Raccoon | Ch. 1 – Introduction to Linear Models with R appeared first on...

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Statistical Reading Rainbow

October 16, 2016
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Statistical Reading Rainbow

For those of us who received statistical training outside of statistics departments, it often emphasized procedures over principles. This entailed that we learned about various statistical techniques and how to perform analysis in a particular statistical software, but glossed over the mechanisms and mathematical statistics underlying these practices. While that training methodology (hereby referred to

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Raccoon: Statistical Models with R free web-book

October 6, 2016
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Raccoon: Statistical Models with R free web-book

Raccoon is a free web-book about Statistical Models with R. Raccoon is the collection of twenty years of notes, exercises and concepts working with statistics and R, and it is part of our web-book project, together with Rabbit: Introduction to R and Ramarro: R for Developers. The post Raccoon: Statistical Models with R free web-book appeared first on...

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Adding polished significance summaries to papers using R

October 4, 2016
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When we teach “R for statistics” to groups of scientists (who tend to be quite well informed in statistics, and just need a bit of help with R) we take the time to re-work some tests of model quality with the appropriate significance tests. We organize the lesson in terms of a larger and more … Continue...

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Binomial Logistic Regression.

October 2, 2016
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Binomial Logistic Regression.

I’m officially a Kaggler. Cut to the good ol’ Titanic challenge. Ol’ is right – It’s been running since 2012 and ends in 3 months! I showed up late to the party. Oh well, I guess it’s full steam ahead from now on. The competition  ‘Machine Learning from Disaster’ asks you to apply machine learning to analyse and…

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Customer Segmentation Part 3: Network Visualization

September 30, 2016
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Customer Segmentation Part 3: Network Visualization

This post is the third and final part in the customer segmentation analysis. The first post focused on K-Means Clustering to segment customers into distinct groups based on purchasing habits. The second post takes a different approach, using Pricipal C...

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One Way Analysis of Variance Exercises

September 30, 2016
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One Way Analysis of Variance Exercises

When we are interested in finding if there is a statistical difference in the mean of two groups we use the t test. When we have more than two groups we cannot use the t test, instead we have to use analysis of variance (ANOVA). In one way ANOVA we have one continuous dependent variable

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Linear mixed-effects model with bootstrapping.

September 23, 2016
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Linear mixed-effects model with bootstrapping.

Dataset here. We are going to perform a linear mixed effects analysis of the relationship between height and treatment of trees, as studied over a period of time. Begin by reading in the data and making sure that the variables have the appropriate datatype. tree<- read.csv(path, header=T,sep=",") tree$ID<- factor(tree$ID) tree$plotnr <- factor(tree$plotnr) Plot the initial graph to…

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