Calculating Land Area and Confidence Interval with Uncertainty Propagation

In summary, the area of a rectangular parcel of land can be computed using the measurements of two adjacent sides, X and Y, which were measured multiple times with the following results: X = 556 m with a standard deviation of 5.3 m and Y = 222 m with a standard deviation of 2.1 m. Using the propagation of uncertainty formula, the estimated area of the land can be calculated at 95% confidence, taking into account the accuracy of the scaled chain at 0.5%. However, it is unclear how to incorporate the accuracy of the chain into the problem, as it is not specified whether it follows a Gaussian or uniform distribution.
  • #1
mx6er2587
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Homework Statement



The area of a flat, rectangular parcel of land is computed from the measurement of the length of two
adjacent sides, X and Y. Measurements are made using a scaled chain accurate to within 0.5% over its
indicated length. The two sides are measured several times with the following results:

X = 556 m
Stdev =5.3 m
n = 8

Y = 222 m
stdev = 2.1 m
n = 7


Estimate the area of the land and state the confidence interval of that measurement at 95%.

Homework Equations



propagation of uncertainty formula


[tex]
\delta z = \sqrt {\left( {\frac{{\partial z}}{{\partial x}}dx} \right)^2 + \left( {\frac{{\partial z}}{{\partial y}}dy} \right)^2 }
[/tex]



The Attempt at a Solution



My issue here is how to account for the accuracy of the chain in the problem statement. I can easily find the values of X&Y at 95% confidence using the mean value and stdev and plug them into the uncertainty formula. What do I do with the 0.5%?
 
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  • #2
It's not clear how the distribution for the scale looks like. If it's a Gaussian distribution with a standard deviation of 0.5 you can simply add that in quadrature. If it's a uniform distribution within +-0.5% it's messy.
 

What is propagation of uncertainty?

Propagation of uncertainty is the process of determining the uncertainty or error associated with a calculated or measured quantity, based on the uncertainties of the input variables or measurements. It involves using mathematical methods to estimate the uncertainty in a final result, taking into account the uncertainties of the individual components.

Why is propagation of uncertainty important?

Propagation of uncertainty is important because it allows scientists and researchers to have a better understanding of the reliability and accuracy of their experimental or calculated results. It also helps in making informed decisions based on the level of uncertainty associated with a particular measurement or calculation.

What are the sources of uncertainty in propagation of uncertainty?

The sources of uncertainty in propagation of uncertainty can include measurement errors, instrument limitations, variations in the experimental conditions, and the inherent variability of the physical system being studied.

How is propagation of uncertainty calculated?

Propagation of uncertainty is calculated using mathematical equations and statistical methods, such as the law of propagation of uncertainty, Taylor series expansion, and Monte Carlo simulations. These methods take into account the uncertainties of the individual components and combine them to estimate the overall uncertainty of the final result.

What are some challenges in propagation of uncertainty?

Some challenges in propagation of uncertainty include the complexity of the mathematical methods involved, the need for accurate and precise measurements, and the difficulty in accounting for all sources of uncertainty. Additionally, it can be challenging to accurately estimate the uncertainty in cases where the relationships between the input variables and the final result are nonlinear or complex.

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