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? Read more…
We at Win-Vector LLC would like to invite our loyal readers to help with our Winter 2010 Subscription Campaign. Please encourage your erudite friends and colleagues to read and subscribe to http://www.win-vector.com/blog/. Read more…
This is an elementary mathematical finance article. This means if you know some math (linear algebra, differential calculus) you can find a quick solution to a simple finance question. The topic was inspired by a recent article in The American Mathematical Monthly (Volume 117, Number 1 January 2010, pp. 3-26): “Find Good Bets in the Lottery, and Why You Shouldn’t Take Them” by Aaron Abrams and Skip Garibaldi which said optimal asset allocation is now an undergraduate exercise. That may well be, but there are a lot of people with very deep mathematical backgrounds that have yet to have seen this. We will fill in the details here. The style is terse, but the content should be about what you would expect from one day of lecture in a mathematical finance course.
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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 “
”.
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.
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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.
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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. Read more…
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. Read more…
We describe the “the local to global principle.” It is a principle used to break algorithmic problem solving into two distinct phases (local criticism followed by global solution) and is an aid both in the design and in the application of algorithms. Instead of giving a formal definition of the principle we quickly define it and discuss a few examples and methods. We have produced both a stand-alone PDF (more legible) and a HTML/blog form (more skimable).
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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.
Read more…