Ballparking Stuart Reges and Kevin Wallace
.
 CSE Home About Us Search Contact Info

This page has a collection of resources from a talk given at the 2016 CS4HS workshop at the University of Washington.

There seems to be an increasing awareness that it is important for students to be able to make what are known as back of the envelope calculations or ballparking. This fits in with a general goal of developing number sense on the part of our students.

An influential computer scientist named Jon Bentley has written about this in an ongoing column known as Programming Pearls. The full column is available in pdf format and is also available as a web page.

Here are some sample questions we discussed:

• How many molecules are there in the human body?
• How many gas stations are there in the US? (Microsoft interview question)
• Making the totally unreasonable assumption that an ordinary sheet of paper could be folded in half an indefinite number of times, each time doubling its thickness, how many times would you have to fold a single sheet of paper in half until it was thick enough to reach from the Earth to the Sun?
• How many hotels are there in the US? (Google interview question)
• How long would it take to sort 1 trillion numbers? (Google interview question)
• How many golf balls can fit in a school bus? (Google interview question)
• How many flights leave SeaTac airport every day?
• How many piano tuners are there in the entire world?
• How many slices of pizza do college students in the US consume each month?
• How many contestants have appeared on the TV show "The Price is Right"? In other words, how many times has the announcer on that show told someone from the audience to "Come on down."
Our colleague Hélène Martin reports that colleagues of hers at Garfield got some interesting results from a challenge asking students to approximate the amount of candy in a jar. The handout is available here.

Stuart mentioned that his favorite estimating trick is that:

210 ≈ 103
There are many computer science algorithms where the number of steps performed for an input of size n will be approximately equal to the log2(n). So for a thousand items, it takes 10 steps. For a million, it takes 20. For a billion, it takes 30. For a trillion, it takes 40. And so on. We like that kind of behavior where you have a small number of steps to perform even when the numbers get very large.

Bonus challenge: Stuart posed a related challenge. Suppose that you were stranded on a desert island and you wanted to have a log table. Not a physical table made out of logs, although that would seem more practical. We want a table showing the logarithm in base 10 for the numbers 1 through 10. If you didn't have a calculator or computer or slide rule or math book with you, how could you compute the logs in base 10? Two are really easy (the logs of 1 and 10). And one of them is easy given Stuart's favorite approximation mentioned above. And how do you get the others? Hint: the number 7 isn't very friendly (old math joke: Why is 6 afraid of 7? Because 7 8 9), but the square of 7 is your friend if you're thinking about approximations (what is it close to?). Stuart's answer can be found here.

Computer scientists are more oriented towards integers and powers of 2, but a lot of people care about real numbers and natural logarithms. Another interesting approximation that can be used for powers of e is that:

e3 ≈ 20
Interestingly enough, since this relates e to a number involving 2 and 10, you can use this approximation in conjunction with the other one to do all sorts of computations.

Stuart Reges