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.

Sudoku Solver

Finished*!!!

Click here:
Sudoku Solver

*This works for almost all Sudoku though there are a few that doesn't work for some reason...blah
**Note the code is really really ugly, I might go back to clean it up or write a neater version (maybe...). and if you have suggestions, please let me know

Food and stories

This is a little experiment I conducted to see how different descriptors of a food effect one's enjoyment and receptiveness of that food. Thank you for all those two participated :)

Both groups were given a little cracker packet, a descriptor, and two short questions that addressed one's enjoyment and receptiveness to the crackers.

Group A (N=10)
Descriptor: Growing up in Taiwan, these rice crackers were special treats for us, especially around b-days.

Group B (N=9)
Descriptor: These rice crackers are light and fluffy with a salty sweet flavor.

Discussion:
-It was difficult to account for many factors such as previous exposures to Asian food and tastes, which probably resulted in the large error bars.
-However, the graph does suggest that although personal enjoyment/taste preference is not effected by the descriptor, one's receptiveness and willingness to try the food again might be increased by a more personal descriptor.
-Need a large N (as always) :P

Actual Driving Times vs Google Driving Times

Data from actual driving times in BLUE.
Google estimated driving times in RED (there was not a with/without traffic distinction for these trips)



Trip A:
Estimated: 10.1 miles in 20 mins


Trip B:
Estimated: 8.6 miles in 17 mins

Commute time to work

Notes:
--Numbers in bold indicate the number of available seats on the Metro
--Times indicate when I left my apt
--N = 9

Conclusion:
--Leave my apt after 8:40 pm
--Collect more data

Solar Powered Alarm "Clock"

I thought it would be fun to make a solar powered alarm "clock" for a Christmas White Elephant exchange. It's not really a clock because it can't tell time but it works!

MPG for my Yaris

I'ved tracked the MPG of my Yaris for the past year :)


(A) Spikes like this one typically happened when I drive home
(B) Lowest MPG occurred during the dead of winter