Predicted Wedding Date

How people's prediction of a friend's wedding compared to the actual wedding date.

Although it was a small data set (N=11), it was interesting to see the distribution between females (who tended to predict the wedding would be sooner) and males.

Party Volume Tracking

Recently I hosted a Bday party for Amy.  I took this opportunity to test out a sensor light system I made.

Although there are limitations, it was still fun to see the data from the party (below).  Enjoy :)

*Data was collected 5 times per second

*Sensor system only had one microphone and was placed on a table by the wall

*Data was smoothed using a running average filter

DC Metro patterns

Using the recent publicly available WMATA (DC's Metro system) ridership data, I generated several network visualizations that highlight many interesting ridership patterns.  For example, you can clearly see that Farragut North, Farragut West, Metro Center, and L'Enfant Plaza are the work destination of many DC commuters.

You can also see that Gallery Place Chinatown, Dupont Circle, U Street, and Clarendon are the central night life locations.

It is also interesting to see the drop off in metro traffic on Sundays compared to Saturdays.

 My class report can be found at: https://wiki.cs.umd.edu/cmsc734_f12/index.php?title=Fong%2C_Allan_-_Metro_Ridership

Wedding day temperature

I wanted to see which weather site was better at forecasting the temperature on our wedding day (6/16/12) in Arlington, VA.  The two sites I monitored were: www.accuweather.com and www.weather.com.

Below is a graph of each day's high/low temperature predictions for 6/16/12.
Note:
The green line is the actual recorded high (82) and low (63) temperatures for 6/16/12.
Weather.com only started updating their predictions on 6/7/12.

The average root mean square of the errors for weather.com and accuweather.com are 2.458 and 4.172 respectively.
Weather.com was better at predicting the high and low temperatures for our wedding day.

Wedding RSVPs

Waiting to get a finalized head count for my wedding was a pretty interesting time.  I wanted to see how quickly it would take to get a finalized head count.

The data below includes only those who were a "Maybe going to the wedding" from an initial survey.

The graphs show how quickly the "Maybe" folks made their decisions.
Families/Couples are considered one unit, one count.
The wedding invites was sent out on 4/18/12 with a requested RSVP by 5/20/12.

The graph below shows the daily count of "Maybe" folk responses.

The graph below is the daily Cumulative Distribution Function of the "Maybe" folk responses.

Responses peak at the beginning of the RSVP phase and in the last week of the RSVP phase.  We received the most responses between 5/20 and 5/22.  Assuming it takes about 1-2 days for the RSVP to be delivered, I'd say that most people responded on-time.  By 5/22, our "Maybe" responses were only 80% complete.  Amy and I followed up with a couple of e-mails helping us finalize our head count shortly after.  Thank you all for your responses and for making this data possible :)

Facebook comments

Last month, Amy and I changed our Facebook status to "Engaged" at the same time: 1415 (2:15pm).
Below is a cumulative distribution plot of Facebook comments on our changed status.
Data was sampled at 30 minute intervals.
















Amy's friends commented much quicker but my page experienced a much more steady inflow of comments.

Bananas

I was curious if there is a way to store bananas to keep them from browning so quickly.
I kept bananas (a) laid flat, (b) in a bag, and (c) suspended for a week.






















Results:
The bananas in the bag kept the longest (apparently oxygen causes bananas to brown).
Bananas laid flat and hung basically browned at the same rate.
Eat your bananas within 6 days of buying them (unless they are kept in a bag).

Bananas laid flat












Bananas in bag












Bananas hung