Calculating Mean Square Error with Differentials

AI Thread Summary
The discussion revolves around calculating mean square error using differentials. The user provides examples with specific values for x and y, detailing how to compute the errors for sums and products. They question whether the same approach applies when dealing with squares, particularly in the context of calculating the area of a rectangle. A suggestion is made to verify the formula for the error in the product of x and y. The conversation emphasizes the importance of correctly applying differential error calculations in various mathematical contexts.
Dell
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when calculating the mean square error i have been using the differential,

if a length measured is x=2 and the error mx=+- 0.005

then x=2+-0.005

if i have x+y where y=3, my=+-0.02

mx+y=\sqrt{m<sub>y</sub><sup>2</sup>+m<sub>x</sub><sup>2</sup>}

mx*y=\sqrt{(y*m<sub>x</sub>)<sup>2</sup>+(x*m<sub>y</sub>)<sup>2</sup>}

but if i have x^2 does this work the same

for example if the area of a rectangle is x*2x can i say 2x2

m2x2=4x*mx
 
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Dell said:
when calculating the mean square error i have been using the differential,

if a length measured is x=2 and the error mx=+- 0.005

then x=2+-0.005

if i have x+y where y=3, my=+-0.02

mx+y=\sqrt{m_y^2+m_x^2}

mx*y=\sqrt{(y*m_x)^2+(x*m_y)^2}

but if i have x^2 does this work the same

for example if the area of a rectangle is x*2x can i say 2x2

m2x2=4x*mx
I don't know if this is correct or not. I suggest looking at what your formula for mx*y, and seeing what you get for mx*x in the formula above.
 
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