# Are you a fair weather fan?

This past weekend my husband and I went first home football game of the season for the Iowa Hawkeyes.  I was excited about the game until I saw the forecast for this past Saturday . . . every day on the news it seemed like the high temperature for the day kept creeping higher and higher.  Finally, on Thursday of last week the air temperature was forecast to reach 94 degrees and the University of Iowa athletic department began posting tips on now to start hydrating now for the game on Saturday.

I called my husband and work and our conversation went something like this:

Me: Did you see the forecast for Saturday?  I think its supposed to be dangerously hot . . .

Husband: Are you trying to bail?

Me: No!  I’m just worried about the heat . . . they said to start hydrating today.

Husband: You’re a fair weather fan!

Really?!? A fair weather fan?

We went.  We had a great time.  The Hawkeyes won.  I didn’t melt.  All in all it was a successful Saturday.

But my experience on Saturday got me thinking about a little thing called the heat index . . . I always thought it of it as the wind chill of really, really hot temperatures.  So, I decided to do a little digging to

1. Find out about the heat index.
2. Prove to my husband that I am not a fair weather fan.

According to the National Weather Service, the calculation of the heat index is a regression equation . . .which quite frankly seemed a little complicated for this blog (but if you’d like to see it, look here).

But, I did think that it was interesting that there was an important note when reading the heat index table . . . the NWS warns that the heat index can only be accurately calculated when the humidity and air temperature are represented on the chart.  In other words, this is a domain and range issue.

There are many situations in mathematics when we’d like to model a particular phenomenon. . .  (heat index, racing times, time-lapse modeling) just to name a few.  And in those situations it does not make sense to have the domain and range be all real number.  Sometimes it doesn’t make sense because the situation represented doesn’t make sense (i.e. negative time) and sometimes it doesn’t make sense because the function will not fit the data as closely if we allow the domain to be all real numbers (as in the case of the racing times).

This past Saturday the temperature at game time was 90 degrees with about 60% humidity . . . we stayed until the end of the third quarter when the Hawkeyes were winning 31-0.  So, what say you . . . am I a fair weather fan?

P.S. If you’re new here, let me let you in on a little secret . . .I love to think about the weather.  Don’t all midwesterners?  Check out a few more weather related posts here and here and here.

# Assigning ZIP Codes

Last week I was working on getting a document together that involved typing many, many ZIP codes from across the United States.  This particular document involved looking up addresses for approximately 350 locations and after a while I realized that I was getting pretty darn good at accurately predicting what the first digit of the ZIP code was going to be and vise versa (i.e. if I looked at the first digit of the ZIP code I could guess the location within a few states).

As I was collecting this data into my spreadsheet, I was developing a hypothesis . . . the first digit of the ZIP code is directly related to the year a state joined the union.

Remember, directly related means as the year the state joined the union increases the first digit of the ZIP code also increases.  In other words, the first digit of the ZIP code depends on the year the state joined the union.  To test my hypothesis I used a map of the U.S. and wrote in the first digits of the ZIP codes I knew.

And then, I created a table of values with the same information (X means I didn’t have the ZIP for any location in that particular state, not that a quick Google search couldn’t have helped me find it, but I just didn’t have it in the document I was working from–also, if my hypothesis proved correct I likely wouldn’t need it!):

 State ZIP Year of Statehood Delaware X 1787 Pennsylvania 1 1787 New Jersey 0 1787 Georgia X 1788 Connecticut 0 1788 Massachusetts 0 1788 Maryland 2 1788 South Carolina 2 1788 New Hampshire X 1788 Virginia 2 1788 New York 1 1788 North Carolina 2 1789 Rhode Island X 1790 Vermont 0 1791 Kentucky X 1792 Tennessee 3 1796 Ohio X 1803 Louisiana 7 1812 Indiana X 1816 Mississippi 4 1817 Illinois 6 1818 Alabama 3 1819 Maine 0 1820 Missouri 6 1821 Arkansas 7 1836 Michigan 4 1837 Florida 3 1845 Texas 7 1845 Iowa 5 1846 Wisconsin X 1848 California 9 1850 Minnesota X 1858 Oregon X 1859 Kansas X 1861 West Virginia X 1863 Nevada X 1864 Nebraska X 1867 Colorado X 1876 North Dakota X 1889 South Dakota X 1889 Montana X 1889 Washington X 1889 Idaho X 1890 Wyoming X 1890 Utah X 1896 Oklahoma 7 1907 New Mexico X 1912 Arizona 8 1912 Alaska X 1959 Hawaii X 1959