Posted on Categories Coding, Rants, StatisticsTags , , , 10 Comments on R annoyances

R annoyances

Readers returning to our blog will know that Win-Vector LLC is fairly “pro-R.” You can take that to mean “in favor or R” or “professionally using R” (both statements are true). Some days we really don’t feel that way. Continue reading R annoyances

Posted on Categories Coding, Computer Science, RantsTags , , ,

Postel’s Law: Not Sure Who To Be Angry With

One of my research interests is finding the principles that underly the management of information, complexity and uncertainty. When something as simple as a web-form is called “technology” it is time to step back and examine your principles. One principle I am not sure about Postel’s law. It doesn’t hold often enough to be relied on and when it fails I am not sure who to be angry with. Continue reading Postel’s Law: Not Sure Who To Be Angry With

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 AdministrativiaTags

Winter 2010 Subscription Campaign

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/. Continue reading Winter 2010 Subscription Campaign

Posted on Categories Finance, Mathematics, TutorialsTags , , ,

“Easy” Portfolio Allocation

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.

Continue reading “Easy” Portfolio Allocation

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