Calculating Mean Square Error with Differentials

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SUMMARY

This discussion focuses on calculating Mean Square Error (MSE) using differentials. The user provides examples involving measurements of length and area, specifically using the values x=2 with an error of ±0.005 and y=3 with an error of ±0.02. The formulas discussed include mx+y=√(my²+mx²) and mx*y=√((y*mx)²+(x*my)²). The user questions the application of these formulas when dealing with squared terms, such as x², and seeks clarification on the correctness of their approach.

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  • Understanding of Mean Square Error (MSE)
  • Familiarity with differential calculus
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Statisticians, data analysts, and students in mathematics or engineering who are working with error analysis and seeking to understand the implications of differentials in Mean Square Error calculations.

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=[tex]\sqrt{m<sub>y</sub><sup>2</sup>+m<sub>x</sub><sup>2</sup>}[/tex]

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

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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Tip: don't use [ sub] and [ sup] tags inside of [ tex] code. I have reformatted your script to make it more readable. Inside of [ tex] tags, use _{...} for subscripts and ^{...} for superscripts.
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=[tex]\sqrt{m_y^2+m_x^2}[/tex]

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

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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