Publications by Ron Pearson (aka TheNoodleDoodler)
The Art of Exploratory Data Analysis
This blog is about the art of exploratory data analysis, which is also the subject of my new book, Exploring Data in Engineering, the Sciences, and Medicine (http://www.oup.com/us/ExploringData). This art is appropriate in situations where you are faced with an existing dataset that you want to understand better. As Stanford University stati...
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Boxplots and Beyond – Part I
Boxplots are a simple and reasonably popular way of summarizing the range of variation of a real-valued variable across different subsets of data. Typical examples might include diastolic blood pressure across a group of patients, broken down by gender and smoking status, or the breaking strength of material samples broken down by ...
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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 and popular, but it does have its limitations. One of those limitations is that the standard boxp...
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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 posts in this series have dealt with the basic boxplot, simple variations like log transformations and v...
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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 essentially combines boxplots with nonparametric density estimates. This post introduces beanplots, a boxp...
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Interestingness Measures
Probably because I first encountered them somewhat late in my professional life, I am fascinated by categorical data types. Without question, my favorite book on the subject is Alan Agresti’s Categorical Data Analysis (Wiley Series in Probability and Statistics), which provides a well-integrated, comprehensive treatment of the analysis of cat...
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Screening for predictive characteristics … and a mea culpa
In my last post, I considered the UCI mushroom dataset and characterized the variables included there using four different interestingness measures. When I began drafting this post, my intention was to consider the question of how the different mushroom characteristics included in this dataset relate to each mushroom’s classification as edibl...
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Measuring association using odds ratios
In my last two posts, I have used the UCI mushroom dataset to illustrate two things. The first was the use of interestingness measures to characterize categorical variables, and the second was the use of binary confidence intervals to visualize the relationship between a categorical predictor variable and a binary response variable. This ...
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Computing Odds Ratios in R
In my last post, I discussed the use of odds ratios to characterize the association between edibility and binary mushroom characteristics for the mushrooms characterized in the UCI mushroom dataset. I did not, however, describe those computations in detail, and the purpose of this post is to give a brief discussion of how they were done. Th...
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The distribution of interestingness
On April 22, David Landy posed a question about the distribution of interestingness values in response to my April 3rd post on “Interestingness Measures.” He noted that the survey paper by Hilderman and Hamilton that I cited there makes the following comment:“Our belief is that a useful measure of interestingness should generate index val...
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