With the well deserved popularity of A/B testing computer scientists are finally becoming practicing statisticians. One part of experiment design that has always been particularly hard to teach is how to pick the size of your sample. The two points that are hard to communicate are that:
- The required sample size is essentially independent of the total population size.
- The required sample size depends strongly on the strength of the effect you are trying to measure.
These things are only hard to explain because the literature is overly technical (too many buzzwords and too many irrelevant concerns) and these misapprehensions can’t be relieved unless you spend some time addressing the legitimate underlying concerns they are standing in for. As usual explanation requires common ground (moving to shared assumptions) not mere technical bullying.
We will try to work through these assumptions and then discuss proper sample size. Continue reading What is a large enough random sample?
This is a tutorial on how to try out a new package in R. The summary is: expect errors, search out errors and don’t start with the built in examples or real data.
Suppose you want to try out a novel statistical technique? A good fraction of the time R is your best bet for a first trial. Take as an example general additive models (“Generalized Additive Models,” Trevor J Hastie, Robert Tibshirani, Statistical Science (1986) vol. 1 (3) pp. 297-318); R has a package named “gam” written by Trevor Hastie himself. But, like most R packages, trying the package from the supplied documentation brings in unfamiliar data and concerns. It is best to start small and quickly test if the package itself is suitable to your needs. We give a quick outline of how to learn such a package and quickly find out if the package is for you.
Continue reading The cranky guide to trying R packages
We discuss a “medium scale data” technique that we call “SQL Screwdriver.”
Previously we discussed some of the issues of large scale data analytics. A lot of the work done at the MapReduce scale is necessarily limited to mere aggregation and report generation. But what of medium scale? That is data too large to perform all steps in your favorite tool (R, Excel or something else) but small enough that you are expected to produce sophisticated models, decisions and analysis. At this scale, if properly prepared, you don’t need large scale tools and their limitations. With extra preparation you can continue to use your preferred tools. We call this the realm of medium scale data and discuss a preparation tool style we call “screwdriver” (as opposed to larger hammers).
We stand the “no SQL” movement on its head and discuss the beneficial use of SQL without a server (as opposed to their vision of a key-value store without SQL). Database servers can be a nuisance- but that is not enough reason to give up the power of relational query languages.
Continue reading SQL Screwdriver
One of the current best tools in the machine learning toolbox is the 1930s statistical technique called logistic regression. We explain how to add professional quality logistic regression to your analytic repertoire and describe a bit beyond that. Continue reading Learn Logistic Regression (and beyond)
We extend the ideas of from Automatic Differentiation with Scala to include the reverse accumulation. Reverse accumulation is a non-obvious improvement to automatic differentiation that can in many cases vastly speed up calculations of gradients. Continue reading Gradients via Reverse Accumulation
This article is a worked-out exercise in applying the Scala type system to solve a small scale optimization problem. For this article we supply complete Scala source code (under a GPLv3 license) and some design discussion. Continue reading Automatic Differentiation with Scala
Having worked with Unix (BSD, HPUX, IRIX, Linux and OSX), Windows (NT4, 2000, XP, Vista and 7) for quite a while I have seen a lot of different software tools. I would like to quickly exhibit my “must have” list. These are the packages that I find to be the single “must have offerings” in a number of categories. I have avoided some categories (such as editors, email programs, programing language, IDEs, photo editors, backup solutions, databases, database tools and web tools) where I have no feeling of having seen a single absolute best offering.
The spirit of the list is to pick items such that: if you disagree with an item in this list then either you are wrong or you know something I would really like to hear about.
Continue reading Must Have Software
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
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