Variance + Standard Deviation

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Transcript Variance + Standard Deviation

Luka Petrović 69/2012
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• The Standard Deviation is a
measure of how spread out numbers are.
• Its symbol is σ (the greek letter sigma)
• The formula is easy:
it is the square root of the Variance.
So now you ask,
"What is the Variance?"
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• The Variance is
average of the squared differences from the Mean.
• 3 simple steps to calculate the Variance:
•
1.
Calculate the
Mean (the simple average of the numbers)
2.
Then for each number:
subtract the Mean and square the result
3.
Then work out the average of (2)
*Reminder:
we will root this value to get Standard Deviation!
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• You and your friends have just measured
the heights of your dogs (in millimeters):
• The heights (at the shoulders) are: 600mm, 470mm, 170mm,
430mm and 300mm.
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• The mean (average) height is 394 mm.
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• Now we calculate each dog's difference from the Mean:
•
• Then square them:
Maxeler part
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• And the Standard Deviation is
just the square root of Variance:
• Standard Deviation:
σ = √21,704 = 147.32...
= 147 (to the nearest mm)
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• Now, we will do this using Maxeler…
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• Kernel code
• Final graph
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• Correct execution!
• Data: Random float values in range 0-100
• Running on MaxCompilerVM-2015,
MaxIDE (Eclipse for Maxeler)
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• Milutinovic, V., et al,
Guide to DataFlow SuperComputing,
Springer, 2015.
• Milutinovic, V., editor,
Advances in Computers: DataFlow,
Elsevier, 2015.
• Milutinovic, V. et al,
Paradigm Shift in SuperComputing: DataFlow vs ControlFlow,
Journal of Big Data, 2015
• http://www.mathsisfun.com/
• http://www.wikipedia.org/
• http://www.investopedia.com/terms/s/standarddeviation.asp
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• Maxeler is fun and we hope we will get more of these:
1. Tutorials
2. Development tools
3. Community help
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