Whats the equation for uncertainties of a gradient?

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SUMMARY

The formula for calculating the uncertainty of a gradient from a graph is essential for accurately determining the moment of inertia (I). The standard deviation of the slope (s_b) is calculated using the formula: s_b = sqrt[(1/(n-2)) * (Σ(y_i - ȳ)² / Σ(x_i - x̄)² - (Σ(x_i - x̄)(y_i - ȳ) / Σ(x_i - x̄)²)²)]. Proper statistical analysis is necessary, and drawing error bars for data points can help visualize the maximum and minimum slopes. This method provides a more accurate representation of uncertainty than arbitrary assumptions.

PREREQUISITES
  • Understanding of linear regression and slope calculation
  • Familiarity with standard deviation and statistical analysis
  • Basic knowledge of error propagation in experimental data
  • Experience with graphing data and interpreting error bars
NEXT STEPS
  • Study the principles of linear regression and slope determination
  • Learn about error propagation techniques in experimental physics
  • Explore the use of statistical software for data analysis
  • Review the section on error analysis in Motulsky's Intuitive Biostatistics
USEFUL FOR

Students and researchers in physics, particularly those involved in experimental design and data analysis, will benefit from this discussion. It is especially relevant for those calculating uncertainties in measurements and interpreting graphical data.

kittassa
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i need to know the formula for calculating the uncertainty of a gradient from a graph. the gradient is being used to calculate the moment of inertia but i can't calculate the error in my I cause i don't know how to calculate the error in my M!

when i did the experiment, i assumed the error to be 5%(ran out of time) and so worked out the I and its uncertainty using that, but now I'm doing a lab report and can't assume, i have to work it out properly.

All i want is the formula, i can do the rest of it... Can anyone help?


Thanks in advance :)
 
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There are many ways of determining the uncertainty of a slope. Without proper statistical analysis, all other methods are approximations. One way is to draw error bars for all your data points. Then draw the maximum possible slope and the minimum possible slope tht could be interpreted with your error bars. Your best fit should be in the middle, and your max and mins would be the plus and minus.

Here's a page with further explanation. See section C at the bottom of the page.
http://www.chemistry.adelaide.edu.au/external/soc-rel/content/datagraph.htm
 
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Fitting a line a+bx to n data pairs x_i,y_i:

a=\bar y-b\bar x

b=\frac{\sum_i(x_i-\bar x)(y_i-\bar y)}{\sum_i(x_i-\bar x)^2}

The standard deviation of the slope is

s_b=\sqrt{\left(\frac{1}{n-2}\right)\left[\frac{\sum_i(y_i-\bar y)^2}{\sum_i(x_i-\bar x)^2}-\left(\frac{\sum_i(x_i-\bar x)(y_i-\bar y)}{\sum_i(x_i-\bar x)^2}\right)^2\right]}

From Motulsky's Intuitive Biostatistics.
 

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