My last four posts have dealt with boxplots and some useful variations on that theme. Just after I finished the series, Tal Galili, who maintains the R-bloggers website, pointed me to a variant I hadn’t seen before. It's called a bee...
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sab-R-metrics: Brief Sidetrack for Scatterplot Matrices
In my last two posts I talked about Ordinary Least Squares, then extended this discussion to the multiple predictor case and briefly talked about some of the problems that may arise. These problems can include omitted variable bias, heteroskedasticity, non-normality, and multicollinearity. Most of these problems are relatively minor in practice and have easy fixes,...
sab-R-metrics: Brief Sidetrack for Scatterplot Matrices
In my last two posts I talked about Ordinary Least Squares, then extended this discussion to the multiple predictor case and briefly talked about some of the problems that may arise. These problems can include omitted variable bias, heteroskedasticity, non-normality, and multicollinearity. Most of these problems are relatively minor in practice and have easy fixes,...
Beeswarm Boxplot (and plotting it with R)
(The image above is called a “Beeswarm Boxplot” , the code for producing this image is provided at the end of this post) The above plot is implemented under different names in different softwares. This “Scatter Dot Beeswarm Box Violin – plot” (in the lack of an agreed upon term) is a one-dimensional scatter plot
Boxplots & Beyond IV: Beanplots
This post is the last in a series of four on boxplots and some of their extensions. Previous posts in this series have discussed basic boxplots, modified boxplots based on a robust asymmetry measure, and violin plots, an alternative that essentia...
Boxplots and Beyond III: Violin Plots
This post is the third in a series of four on boxplots and closely related data visualization techniques for comparing subsets of a dataset, or comparing different datasets that we hope or expect to be similarly distributed. The previous two post...
Using R for Introductory Statistics, Chapter 5, Probability Distributions
In Chapter 5 of Using R for Introductory Statistics we get a brief introduction to probability and, as part of that, a few common probability distributions. Specifically, the normal, binomial, exponential and lognormal distributions make an appearance.
For each distribution, R provides four functions whose names start with the letters d, p, q or r followed by...
Using R for Introductory Statistics, Chapter 5, Probability Distributions
In Chapter 5 of Using R for Introductory Statistics we get a brief introduction to probability and, as part of that, a few common probability distributions. Specifically, the normal, binomial, exponential and lognormal distributions make an appearance....
Boxplots and Beyond – Part II: Asymmetry
In my last post, I discussed boxplots in their simplest forms, illustrating some of the useful options available with the boxplot command in the open-source statistical software package R. As I noted in that post, the basic boxplot is both useful...
Clustering NHL Skaters
I have been sitting on this post for some time now and wanted to get it out there. The goal is to simply show how easy it is to pull live data from the web into R, massage it, and perform some analytics on it. I am not sure how useful this analysis really is 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
