Sometimes you need to draw a simple network diagram, like this Hasse diagram
but you don’t have a good graph drawing tool. Get Graphviz! Easy to learn, scriptable, and FREE.
Here’s a plain-English metaphor:
- What does the Fourier Transform do? Given a smoothie, it finds the recipe.
- How? Run the smoothie through filters to extract each ingredient.
- Why? Recipes are easier to analyze, compare, and modify than the smoothie itself.
- How do we get the smoothie back? Blend the ingredients.
Here’s the “math English” version of the above:
- The Fourier Transform takes a time-based pattern, measures every possible cycle, and returns the overall “cycle recipe” (the amplitude, offset, & rotation speed for every cycle that was found).
Tip from Kotke, who has a cool Fourier Transform video.
The Justice Department and the Census Bureau are engaged in a kerfuffle over the 2020 Census. It’s all about a question of citizenship: “What country are you a citizen of?” With the inevitable congressional reapportionment that occurs based on the Census, this is a question that many states really don’t want to know the answer to.
My take: the Census Bureau has been crying poor for years now. The Trump Administration should jawbone Congress into increasing the Bureau’s funding, but only if they ASK THE QUESTION (and report the answers).
Update: Now folks should really be worried. Combine citizenship data with Google location data (“we have ways to make you opt in”), and some dedicated data miners could find every Android-using illegal alien in the country.
Tips from the Instapundit, where the signal-to-noise ratio seems to be increasing lately.
Update: It has come to my attention that at least one other branch of the federal government already ASKS THE QUESTION, to wit, the Justice Department’s Bureau of Alcohol, Tobacco, Firearms, and Explosives* E-Form 4473, Firearms Transaction Record has Questions 12 and 13:
In other words, you cannot exercise your 2nd Amendment right to own a firearm unless you ANSWER THE QUESTION.
* Or what I call a Redneck Hedge Fund.
Want to save the planet? How about starting by saving the birds. Here’s a Pareto graph that gives a strong hint of where to start:
That’s right, get the cat population under control. Eradicate feral cat colonies, and euthanize cat collections (oh, and institutionalize obsessive cat ladies). The whole country needs to grow up and get that “cute little kitty” lie out of their heads, and replace it with something more realistic, like “bird murderer.”
Update: One Dallas suburb is infested with feral cats, protected by a well-connected cat lady.
Five very interesting articles recently popped up on the web, suggesting that current science is much more interesting than the average Joe might think:
*ESPN’s website that analyzes sport statistics, election polling, and (apparently) anything else that catches their analysts’ eyes.
…everything looks like a nail.
Daniel Lakens, the 20% Statistician, takes a rare but easy shot at statisticians and null hypothesis significance testing.
Our statistics education turns a blind eye to training people how to ask a good question. After a brief explanation of what a mean is, and a pit-stop at the normal distribution, we jump through as many tests as we can fit in the number of weeks we are teaching. We are training students to perform tests, but not to ask questions
…the Statisticians’ Fallacy: Statisticians who tell you ‘what you really want to know’, instead of explaining how to ask one specific kind of question from your data.
My favorite is the two-tailed test of the difference of two means, which can provide evidence that the two are different, but not that they are (nearly) the same. My runners up are goodness-of-fit tests, which do no such thing. Sometimes I feel like I’m selling the researcher’s version of Snake Oil, rather than teaching sound data analysis and interpretation.
Lakens closes with an excellent addendum, a reference to David Hand’s Deconstructing Statistical Questions, which goes into much more detail.
Wisdom hath built her house, she hath hewn out her seven pillars. –Proverbs 9:1
I just finished Stephen Stigler’s The Seven Pillars of Statistical Wisdom, and I’m daunted–and embarrassed that I waited so long to read it. Stigler gives us a structure and taxonomy to statistical thinking* that gives us the “big picture” of statistics.
Quite a difference from the descriptives-to-inference-to-models approach that most textbook authors follow. This is making me rethink how I approach my introductory courses, especially those for statistics majors. I’m starting with a baby step: adding the (inexpensive, paperbound) book as a required reading in my statistical research methods class.
*the 7 pillars: aggregation, information, likelihood, intercomparison, regression, design, and residual (and that’s just the table of contents!)