I am going to measure the effects that added weight of marbles have on the amount of time it takes a paper boat to sink in a bathtub. I will start with 30 paper boats all made as close to identical as possible. The first boat will hold one marble, the second two marbles and so forth up to 30 marbles. A stopwatch will measure the time from entrance into the water until the peak of the cabin on the boat is submerged. I expect that the data will show a curved near exponential as to the time it takes to submerge the boat. (Note: I intend to look into the weight of marbles and may need to use something else as cool, but with less weight per object).
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Material:
  • 35 sheets of construction paper
  • Basic origami skills (Origami Boat Instructions)
  • 35 marbles (5 grams each)
  • Stop Watch (I used my cell phone and multiple online stopwatches)
  • Bathtub full of water
  • A couple of towels
Hypothesis:
More weight in a paper boat should make it sink with a negative association, close to linear once the weight is great enough
to overcome the buoyancy of the paper itself.
Experimental Variable:
Amount of weight in the boat (adding one marble adds 5 grams)
Response Variable:
Time it takes each boat to sink in seconds.

Once I started making boat in mass I realized I didn't need the tape to hold the sides in, I could just pinch the
cabin of the boat and all would work well, so ended up with 35 of these...
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Part of the fleet.

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The first boat...

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... with one marbel.



As luck would have it both the one marble boat and the two marble boats were buoyant and never sank, so I ended up making a total of 35 boats to have enough data.

I ended up removing the one marble and two marble boats from the data as plotting infinity tends to hose your axis scale ;-)


Here's a video of the 35 marble boat and what is supposed to happen.


Here's a shot of the recovered boats...
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Hope you enjoy it as much as I did.

Andy's Data and Analysis
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]]