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To calculate these answers, I added up all of the entries in each row.
For example, in this row, I added up these numbers divided by 5,
and they give me a result of 4.00 here, 9.00 here, and 16.02. I’ve rounded here for this last one.
Now, look at this data, isn’t this beautiful? 1, 4, 9, 16.02 though.
Well, that 0.02 is sort of a bummer but let’s put it was just 16 per seconds
and see if there is any pattern.
Well 1, 4, 9, 16, I do see a pattern there. These are squares.
One unit, 1 unit of time squared is 1, 2 squared is 4, 3 squared is 9,
and 4 squared is 16--that’s an incredible pattern.
It seems like there may be some relationship between distance and square of time,
but the 16.02 is really bumming me out.
Does it really mean that this pattern is false?
Well, not necessarily because one thing we forgotten to do so far is calculate error.
In the previous scene, we talked about how to calculate the error of a single measurement.
May be that measurement measuring the length of the shadow or something.
But now, we have multiple measurements with five measurements for each of these calculated values.
How do we calculate the error associated with multiple measurements?