Finding uncertainty for varying error bars

In summary, the conversation discusses an experiment on Compton scattering and the attempt to find the value of the gradient and y-intercept with their corresponding uncertainties using physically obtained values instead of statistical uncertainties. The equation used is 1/E' = 1/E + 1/mc2(1-cosθ) and the gradient is 1/E while the y-intercept is 1/mc2. The possibility of finding a function in Excel or Mathematica to quickly find the lines with maximum/minimum gradient is also discussed, as well as the use of a weighted regression fit. The uncertainty for (1-cosθ) increases with θ and the uncertainty relationship for E' is more complex. The use of multiple readings will not
  • #1
joelwong
2
0

Homework Statement


I am currently doing an experiment on Compton scattering and have plotted a linear graph of 1/E' on (1-cos θ), where E' is the scattered gamma ray energy. My goal is to find the value of the gradient and y-intercept with their corresponding uncertainties. Instead of using the statistical uncertainty as given by excel's LINEST function, I want to use the physically obtained ones. i.e. Due to the limitations of the apparatus. This has resulted in varying error bars for each point, which makes it very difficult to find the lines with max/min gradient. Is there any function in excel to find the max/min gradient lines?

I use Mathematica too if it helps.

Homework Equations


The equation is:

1/E' = 1/E + 1/mc2(1-cos θ)

The gradient is 1/E and the y-intercept is 1/mc2

The Attempt at a Solution


I know I can probably do it the brute force way- adjusting the lines until it fits within the error bars. But I' ve encountered this a few times already and I'm wondering if there is a function to do it quickly.
 
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  • #2
A crude way is to repeat data points according to the degree of certainty. Can you figure out the relationship?
But what you really want is a weighted regression fit. I don't know if Mathematica offers that. If not, you could write some spreadsheet formulas to do it.

And welcome to PF.
 
  • #3
Thanks haruspex for the reply.

I'm not too sure what you mean by repeat data points according to the degree of uncertainty.

The uncertainty for (1-cosθ) is simple, d(1-cosθ) = sinθ dθ, so the uncertainty increases with θ. I have taken results for 15°, 30°, 45°,...

The uncertainty relationship for E' is not as simple- it is based on the FWHM of each photopeak of the spectrum, so there is no clear relationship.

The spectrum is taken by using a NaI(Tl) detector over a period of time. Thus, multiple readings will not make any difference, since every time period is independent of the other. Thus, the data points I have obtained over 180s are total counts over that period of time. Taking multiples readings and adding them up will get me the same result.

Note: I decided to do it the brute force way for now, since I have to submit the report. But I still welcome any responses, since it's something good to learn. I'll see if I can craft some excel function to do the trick when I have the time.
 

1. How do you calculate uncertainty for varying error bars?

Uncertainty for varying error bars is typically calculated by determining the standard deviation of the data points within each group or category. This can be done using statistical software or manually using the formula for standard deviation. The uncertainty is then represented by the error bars on the graph, which show the range of values that the data points may fall within.

2. Why is it important to include uncertainty in data analysis?

Including uncertainty in data analysis is important because it provides a measure of the reliability and accuracy of the data. It allows for a better understanding of the potential variability in the data and helps to identify any potential sources of error or bias in the measurements. Additionally, including uncertainty helps to communicate the limitations of the data and enables more informed decision making.

3. Can uncertainty be reduced or eliminated in data analysis?

No, uncertainty is an inherent part of any measurement and cannot be completely eliminated. However, it can be reduced by increasing the precision and accuracy of the measurements and by reducing sources of error. It is important to consider and account for uncertainty in data analysis rather than trying to eliminate it.

4. How does the size of error bars relate to uncertainty?

The size of error bars is directly related to the uncertainty of the data. A larger error bar indicates a higher level of uncertainty, while a smaller error bar indicates a lower level of uncertainty. This is because larger error bars represent a wider range of potential values for the data points, while smaller error bars represent a more precise measurement with a smaller range of potential values.

5. Are there different types of uncertainty that can be represented by error bars?

Yes, there are different types of uncertainty that can be represented by error bars, such as random error, systematic error, and measurement error. Random error is due to natural variability in the data and can be reduced by taking multiple measurements. Systematic error is due to a consistent bias in the measurements and can be reduced by adjusting the measurement method. Measurement error is due to limitations of the measurement equipment and can be reduced by using more precise equipment.

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