Math teachers have been using the “German Tank Problem” for awhile to teach estimators. It goes something like this.

The Allies capture five German tanks. Suppose that the serial numbers on the tanks are 15, 23, 59, 83, and 109. Provide an estimate of the number of tanks that were produced.

You can see that your estimate would be a great deal lower than the estimate if the serial numbers were 1015, 2394, and 9438.

The story and accompaning math problem was written about in the Guardian. My favorite line, which turned out to be true, was this:

The statisticians believed that the Germans, being Germans, had logically numbered their tanks in the order in which they were produced.

It turns out that the statisticians were spot on:

By using this formula, statisticians reportedly estimated that the Germans produced 246 tanks per month between June 1940 and September 1942…

There are currently 174 books on my Amazon wishlist that I could order directly from Amazon. (My wishlist has a total of 195 books, but 21 are only available from other sellers.) Total price is approximately $3,549 (I rounded all prices to whole dollars), for a mean of approximately $20 per book.

But the median price of a book on my wishlist is (again to the nearest whole dollar) $16; the difference between the median and the mean is a hint that the distribution is skewed. And there are actually two peaks — one centered on $10 and one centered on $16-17. The distribution looks like this:

I’ve cut off the histogram at $100, which omits Mitchell’s Machine Learning at a list price of $168.16. Here’s a zoomed-in version omitting the 23 most expensive (all those over $30):

The two peaks are easy to explain: paperbacks and hardcovers, respectively. The…

This is what I see when i think about higher education in this country today:

Remember the housing meltdown ? Tough to forget isn’t it. The formula for the housing boom and bust was simple. A lot of easy money being lent to buyers who couldn’t afford the money they were borrowing. That money was then spent on homes with the expectation that the price of the home would go up and it could easily be flipped or refinanced at a profit.  Who cares if you couldn’t afford the loan. As long as prices kept on going up, everyone was happy. And prices kept on going up. And as long as pricing kept on going up real estate agents kept on selling homes and finding money for buyers.

Until the easy money stopped.  When easy money stopped, buyers couldn’t sell. They couldn’t refinance.  First sales slowed, then prices started falling…

Robert Sedgewick has the slides for a talk, Algorithms for the Masses on his web site.

My favorite slide is the one titled “O-notation considered harmful” — Sedgewick observes that it’s more useful to say that the running time of an algortihm is ~aN^c (and back this up with actual evidence from running the algorithm) than to have a theorem that it’s O(N^c) (based on a model of computation that may or may not be true in practice).

The serious point of the talk, though, is that everyone should learn some computer science, preferably in the context of intellectually interesting real-world applications. This is what Sedgewick is doing in his Princeton course and in his book with Kevin Wayne, Algorithms, 4th edition, which I confess I have not read. There’s a Coursera course, in six-week parts, starting in August and November respectively. For a lot of…