Sport Entertainment

It seems like no matter what time of year it is, there is always some kind of major sporting event to watch. So I was curious if this was actually true or just my perception. Below I plotted the typical "Amount of Sport Entertainment" in given year for the 4 major sporting leagues in the US (NBA, NFL, MBL, and NHL).

There is definitely a low in the June/July time frame. Maybe the sports industry does not want to compete with the summer movies.

pre = preseason = .5
r = regular season = 1
post = post season = 1.5
f = finals = 1.5

Jan.1 = first half of January
Jan.2 = second half of January

Birthday Posts

You know how on your b-day, you typically get a ton of Birthday wishes on Facebook?
Well, I was curious if there was a pattern to this madness so I graphed the distribution of facebook birthday messages for each hour on the day of my birthday (by percentage).

There appears to be two peaks around 2PM and 9PM. I wonder if that is suggestive of the times when most people check their facebook.

Salt intake

Inspired by the recent dietary guidelines (http://www.cnpp.usda.gov/dietaryguidelines.htm), I wanted to see how good people are at estimating the amount of salt in food. Below are box-plots of the percentage of one's daily recommended value of salt (based on a 2,000 calorie diet) people THOUGHT were contained in a serving size for the different foods. In addition to what people thought, the green squares represent the ACTUAL percentage of one's daily recommended value of salt.
















It is interesting that, people typically over-estimate the amount of salt included in the foods (except for the bagel and black beans), but at the same time, people thought our daily recommended value of salt is 5.43 grams while in reality it is 2.4 grams or 1 teaspoon.
N = 11

Items include (serving size):
Thomas plain bagel (1 bagel)
Bush's black beans (1/2 cup)
Campbell's tomato soup (1 cup)
Utz wheat n cheddar crackers (1 pkg)
Oscar Mayor bologna (1 slice)
Snyder's sourdough nibblers (16 nibblers)
Peter Pan crunchy peanut butter (2 tbsp)
Planters Lightly salted cashews (1 oz)
Oreo (3 cookies)

Songs during Glee

I like the songs in Glee but not so much the storyline and drama. If I can figure out when the songs typically occur, then I can tune in when there is the greatest probability that there will be a song and not have to sit through the drama. The best chances of catching a song appears to be 11, 27, and 41 minutes into the show.

Average number of song per show: 6.2 songs
Average duration of songs: 1.8 minutes (stdv of 0.6 minutes)
N = 6 shows
I know I need a larger sample size, anyone wanna help me collect data? :)

Overlap in TV channels

I am always curious how TV channels overlapped in terms of similar "themed" shows. I looked at all the shows currently (2010) on for three of my favorite channels (Food Network, Science Channel, and Travel Channel) and mapped the number of what I considered "unique" and "overlapping" shows into a venn-diagram.

The most overlap was between the Food Network and the Travel Channel...probably because traveling and culture are so tightly linked with food. The Food Network also has the most shows, probably an indication of its willingness to explore with different themed shows and ideas.

When babies are born

Graph below shows that most of my friends have birthdays in late fall or in March. There is also an increasing trend in birthdays from May to November. Kind of interesting assuming that most babies are born 9 month after conception :P

Baby Sleep Patterns

Questions:
How do variables, such as number of naps during the day or time of sleep, affect the time Baby wakes up the next day?

Independent variables:
A - Number of naps during the day before
B - Total time (mins) napping the day before
C - Amount of time (mins) awake between last nap and sleep
D - Time of sleep
E - Amount of sleep (mins) that night
F - Amount of time (mins) awake during the night
G - Month

Dependent variable:
Time Baby wakes up

Overall results:
Independent variables are "Okay" at predicting when Baby wakes up. Results are useful to inform but not drive decisions. The effects are not statistically significant and models have mediocre predictive powers, probably due to other unaccounted confounding factors.

ANOVA results/discussions:
-Though, none of the variables had a statistically significant effect, factors C (amount of time awake between last nap and sleep) and F (amount of time awake during the night) tend to have the most effect on when Baby wakes up.
-Results could probably improve if I used time of sleep as a covariate ("future work" hehe).

Classification Tree results/discussions:
-Sleep time before or after 9:22pm has a big effect as to when Baby wakes up.
-If Baby is awake for more than 292.5 mins (4.9 hrs) before sleeping, Baby will tend to wake up later.
-As seem from the second graph below, this classification tree model tends to under-predict when Baby wakes up.



Neural Network results/discussions:
-Factors used in this model are "okay" as predicting when Baby wakes up.
-Test correlation was 0.88 which is pretty good. However, the overall performance was not great (R=0.5) probably due to over-training of the network.