Posted on Categories Applications, Expository Writing, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, Statistics To English TranslationTags , , , , , ,

Living in A Lognormal World

Recently, we had a client come to us with (among other things) the following question:
Who is more valuable, Customer Type A, or Customer Type B?

This client already tracked the net profit and loss generated by every customer who used his services, and had begun to analyze his customers by group. He was especially interested in Customer Type A; his gut instinct told him that Type A customers were quite profitable compared to the others (Type B) and he wanted to back up this feeling with numbers.

He found that, on average, Type A customers generate about $92 profit per month, and Type B customers average about $115 per month (The data and figures that we are using in this discussion aren’t actual client data, of course, but a notional example). He also found that while Type A customers make up about 4% of the customer base, they generate less than 4% of the net profit per month. So Type A customers actually seem to be less profitable than Type B customers. Apparently, our client was mistaken.

Or was he? Continue reading Living in A Lognormal World

Posted on Categories Opinion, Rants, StatisticsTags , , , , 1 Comment on Relative returns: a banker versus trader paradox

Relative returns: a banker versus trader paradox

Quick Joke.

Q: What is the difference between a banker and a trader?
A: A banker will try and tell you a 10% loss followed by a 10% gain is breaking even.

Continue reading Relative returns: a banker versus trader paradox

Posted on Categories Applications, Expository Writing, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, Statistics To English TranslationTags , , , ,

Statistics to English Translation, Part 2b: Calculating Significance

In the previous installment of the Statistics to English Translation, we discussed the technical meaning of the term ”significant”. In this installment, we look at how significance is calculated. This article will be a little more technically detailed than the last one, but our primary goal is still to help you decipher statements about significance in research papers: statements like “
$ (F(2, 864) = 6.6, p = 0.0014)$ ”.

As in the last article, we will concentrate on situations where we want to test the difference of means. You should read that previous article first, so you are familiar with the terminology that we use in this one.

A pdf version of this current article can be found here.
Continue reading Statistics to English Translation, Part 2b: Calculating Significance

Posted on Categories Rants, StatisticsTags , , 3 Comments on CRU graph yet again (with R)

CRU graph yet again (with R)

IowaHawk has a excellent article attempting to reproduce the infamous CRU climate graph using OpenOffice: Fables of the Reconstruction. We thought we would show how to produced similarly bad results using R.
Continue reading CRU graph yet again (with R)

Posted on Categories Applications, Expository Writing, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, Statistics To English TranslationTags , , , 4 Comments on Statistics to English Translation, Part 2a: ’Significant’ Doesn’t Always Mean ’Important’

Statistics to English Translation, Part 2a: ’Significant’ Doesn’t Always Mean ’Important’

In this installment of our ongoing Statistics to English Translation series1, we will look at the technical meaning of the term ”significant”. As you might expect, what it means in statistics is not exactly what it means in everyday language.

As always, a pdf version of this article is available as well. Continue reading Statistics to English Translation, Part 2a: ’Significant’ Doesn’t Always Mean ’Important’

Posted on Categories Coding, Statistics, TutorialsTags , 4 Comments on R examine objects tutorial

R examine objects tutorial

This article is quick concrete example of how to use the techniques from Survive R to lower the steepness of The R Project for Statistical Computing‘s learning curve (so an apology to all readers who are not interested in R). What follows is for people who already use R and want to achieve more control of the software. Continue reading R examine objects tutorial

Posted on Categories Applications, Expository Writing, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, Statistics To English TranslationTags , , , , , , 4 Comments on “I don’t think that means what you think it means;” Statistics to English Translation, Part 1: Accuracy Measures

“I don’t think that means what you think it means;” Statistics to English Translation, Part 1: Accuracy Measures

Scientists, engineers, and statisticians share similar concerns about evaluating the accuracy of their results, but they don’t always talk about it in the same language. This can lead to misunderstandings when reading across disciplines, and the problem is exacerbated when technical work is communicated to and by the popular media.

The “Statistics to English Translation” series is a new set of articles that we will be posting from time to time, as an attempt to bridge the language gaps. Our goal is to increase statistical literacy: we hope that you will find it easier to read and understand the statistical results in research papers, even if you can’t replicate the analyses. We also hope that you will be able to read popular media accounts of statistical and scientific results more critically, and to recognize common misunderstandings when they occur.

The first installment discusses some different accuracy measures that are commonly used in various research communities, and how they are related to each other. There is also a more legible PDF version of the article here.

Continue reading “I don’t think that means what you think it means;” Statistics to English Translation, Part 1: Accuracy Measures

Posted on Categories Expository Writing, Quantitative Finance, StatisticsTags , , , , 2 Comments on What is the gambler’s equivalent of Amdahl’s Law?

What is the gambler’s equivalent of Amdahl’s Law?

While executing some statistical detective work for a client we had a major “aha!” moment and realized something like “Amdahl’s Law” rephrased in terms of probability would solve everything. We finished our work using direct methods and moved on. But it is an interesting question: what is the probabilist’s (or gambler’s) equivalent of Amdahl’s Law? Continue reading What is the gambler’s equivalent of Amdahl’s Law?

Posted on Categories Pragmatic Machine Learning, StatisticsTags 22 Comments on Survive R

Survive R

New PDF slides version (presented at the Bay Area R Users Meetup October 13, 2009).

We at Win-Vector LLC appear to like R a bit more than some of our, perhaps wiser, colleagues ( see: Choose your weapon: Matlab, R or something else? and R and data ). While we do like R (see: Exciting Technique #1: The “R” language ) we also understand the need to defend oneself against the abuse regularly dished out by R. Here we will quickly share a few fighting techniques.
Continue reading Survive R

Posted on Categories Exciting Techniques, Expository Writing, Mathematics, Pragmatic Data Science, Pragmatic Machine Learning, StatisticsTags , , , , , , 7 Comments on Good Graphs: Graphical Perception and Data Visualization

Good Graphs: Graphical Perception and Data Visualization

What makes a good graph? When faced with a slew of numeric data, graphical visualization can be a more efficient way of getting a feel for the data than going through the rows of a spreadsheet. But do we know if we are getting an accurate or useful picture? How do we pick an effective visualization that neither obscures important details, or drowns us in confusing clutter? In 1968, William Cleveland published a text called The Elements of Graphing Data, inspired by Strunk and White’s classic writing handbook The Elements of Style . The Elements of Graphing Data puts forward Cleveland’s philosophy about how to produce good, clear graphs — not only for presenting one’s experimental results to peers, but also for the purposes of data analysis and exploration. Cleveland’s approach is based on a theory of graphical perception: how well the human perceptual system accomplishes certain tasks involved in reading a graph. For a given data analysis task, the goal is to align the information being presented with the perceptual tasks the viewer accomplishes the best. Continue reading Good Graphs: Graphical Perception and Data Visualization