Site is moving

Hi to anyone actually reading this blog.  I have moved the site to my own domain, and you can find it now at

http://jrummel.com

Communicating business dealings

We do quite a bit of spreadsheet analysis and case discussions in my class, and sometimes I have students make presentations of their analysis in a way that they are "selling" their analysis to the managers with the decision rights.  They spend time fine tuning their presentations to be more convincing than other groups.

But sometimes they fall into bad habits, using stale jargon and omitting crucial assumptions.  Here's an example from the WSJ of our friends at Goldman Sachs, making a pitch that is very similar to a famous pitch that everyone with an email account has probably seen.

Made me smile.

What does free mean?

I don't have very many, but the ios/droid app marketplace is interesting with all the free apps.  I find myself hesitating to pay money for an app, even though having it would be quite useful and I get that the person who wrote it is trying to make a living.  I'll drop $2 on a cup of coffee, but resist paying 99 cents for a app I know I'll use?

The I saw this article about trying to monetize blogging and was taken by this quote:

If you are not paying for it, you’re not the customer; you’re the product being sold.

 Interesting thought now when loading a free app.  Do you wonder who is buying you?

Using the brain to develop decision algorithms

There is not enough information in this article to know the details, but the inventor of the PalmPilot has a new venture to model decision making.

College football decision trees

Here's a nice little article on using decision trees to predict football outcomes.  Thanks JD!

Math music video

This is a little weird, but kind of cool.  Songs about Mandlebrot Sets:

Jonathan Coulton - Mandlebrot Set

Football math

UConn surprised everyone for a fourth-down conversion late in the game against Pittsburgh.  Here's the math for doing this more in football.

Goizueta in the news

Some nice press for the undergraduate program here from the Fiske Guide to Colleges. 

Time series graph

There are lots of time series graphs available, but sometimes there are more issues that you want to include.  This is sometimes accomplished with bubble charts, but here's a nice example using geography as the "x-axis":

Graphs can say one thing, or another

First, I am not posting this to make any statement about Paul Krugman, or anything like that.  But when comparing time series data, choosing the starting point can be more imporant than it seems, as this blogger demonstrates.  You could imagine investing strategies being compared in a similar fashion.  One graph shows strategy A is better, but with the same data and a different start date, another graph would show that strategy B is better. 

Another issue is the choice of the other data sets - they can also be chosen to look "fair" but those time series contain special features that make the analysis less than fair.  For example, compare company X with specially chosen company Y, because of the special charges for Y in a particular quarter make some observation about X seem more true.

Simple comparisons are not always so simple.

A post about something I know little about

One of the questions I like to pose in my classes is "how do you know?"  Doing analysis of data and possible decision leads to lots of wild speculation, and this question sometimes brings us back to recognizing that widely held theories are actually quite tenuous.  One observation can bring down a whole theory, a "black swan" event.  I like to use examples from physics, even though I am not a physicist, because I find that students often think that we "know" stuff there.  Science is settled, even if other areas are not.

Here is an interesting (to me) article about measuring light and comparing the measurements to accepted physical theories.  At the end, something to think about:

There’s also a possibility that the explanation could be even more far-reaching, such as that the universe is not expanding and that the big bang theory is wrong.

How certain are you of the things you know?

Subprime mortgages and CDS

Here is an interesting interview with Michael Lewis about the financial meltdown.  Part of it has to do with how so many people made so many dumb mistakes.  I hope this makes people more skeptical of their decisions, and do a bit more analysis and think about how it could go wrong.

Costs and risks

The "Top Gear" guy had a nice rant in the Times Online about laws to mitigate risks.  Can we think of a way to connect the risk with the costs?  Suppose that we could eliminate a risk entirely if everyone just paid a small price.  Think polio, for example.  Well, then what about this risk, or that risk.  Surely the same solution applies, right?  As Jeremy points out, we all take our shoes off at the airport now, so the terrorists decided to use their underwear.  What will we have to do now, if risk elimination is the goal.

Or are there risks that we just have to live with?  Where do we draw the line?  Can we use probabilities and decision analysis to help make policies a little more bearable?

Rankings

Here is the most admired companies list for this year.  There are quite a few ways to set up the mechanics for the 'scoring' and there is not much in the way of confidence intervals in the article.  Would the article be more or less effective if there were statistical ties in the rankings?  How many ties would there be?

Here are the undergraduate business school rankings for 2010.

Christian statistics

Not statistics that act in a Christian way, but rather trying to use statistics to understand the state of Christianity.  Is it growing?  Shrinking?  Healthy?  Dying?  Turns out that describing statistics collected and making inferences is tricky, and the link describes some nice examples of where things can go wrong.  Thanks to Brad for the link.

When is an average or not an average?

First, I have not followed up on the reference in this Instapundit blog post, so I do not know whether this is really the way the British Met Office really computes average temperatures:

In fact, the Met still asserts we are in the midst of an unusually warm winter — as one of its staffers sniffily protested in an internet posting to a newspaper last week: “This will be the warmest winter in living memory, the data has already been recorded. For your information, we take the highest 15 readings between November and March and then produce an average. As November was a very seasonally warm month, then all the data will come from those readings.”

But suppose for a minute that an average for a whole season (year) really is fifteen days, and that the highest fifteen all (most) came from November. Would that really represent the average for the winter? Can you think about why they would not use every day's reading from November through March? Should they use just the high for the day, or the high and the low for each day? And where should the reading come from? How many locations around England would be enough for what would seem like a good average calculation? What if averages in the past were computed differently? Could you make comparisons?

Average seems like such an easy concept - but it often is quite tricky. To say nothing about trying to understand the variability of temperatures (do they compute standard deviations?)

For example, the fifteen day record could occur in a season where the other one hundred and thirty five days were pretty cold...

Something a bit different

Most of the posts here are about what I do in the classroom, mostly geek stuff. But I am also a Christian who believes God plays an active role in my life. I have experienced too much to doubt this.

But it is also true that as a professor, there is a dynamic that I have to be careful about with students. Since I have the rights to assign grades, I try to be careful not to make students feel that their beliefs might impact their grades. I work with students from too many different backgrounds and I never want them to worry that their faith will be an issue with me.

On the other hand, I also try not to hide my Christian beliefs, and the way that affects how I live my life and treat others.

The dust-up over Brit Hume's comments about Tiger Woods at first seemed to be the kind of thing I sometimes worry about: how can a Christian tell a Buddhist what to do?

This article from Michael Gerson in the Washington Post is a great way of thinking about this (I think). In the absence of coercive power, why wouldn't someone offer a life preserver that worked for them to another person in pain?

UPDATE: Brit's comments.

This is not how I do it

I could never get a good distribution this way, so I just read them one page at a time.

What do we know?

I am naturally somewhat of a skeptic, but it is hard to not read something and think about how smart the scientists are, and how much they know, and what will soon be possible. One of the things that is really interesting is brain research. Suppose we could know how we work?

All kinds of new tools allow researchers to "watch" your brain work. Or can they? How do they "know"?

Here some interesting reading about the difficulties in measuring, knowing and statistics.

Here is another that makes you think twice when we assume that science is a clean process that follows a straight line to the truth, getting it right along the way.

And here's a cartoon view of how this works (or doesn't work).

Cartoon with statistics

This cartoon site has quite a bit of "geek" humor, so I like it. It also has an interesting feature where if you move the mouse over the cartoon, a hidden message appears. This one has a comment about "the mother of all sampling biases." It may not seem funny, but it is a really great example. Some of his other cartoons are pretty good. This one makes me wonder about teaching statistics.