How to Combine Gradient Uncertainty with other Uncertainty?

In summary, the speaker conducted an experiment to measure the speed of sound using two microphones and a hammer. They created an Excel graph with distance on the x-axis and time on the y-axis, and added a line of best fit. The speaker is now seeking advice on how to include error bars and calculate the uncertainty of the gradient, taking into account random uncertainty, calibration uncertainty, and scale reading uncertainty from the meter stick. They have experimented with combining the uncertainties and plotting them as custom error bars in Excel, but are unsure if this is the correct method. They are also unsure how to find the uncertainty of the gradient with the added error bars.
  • #1
Banker
27
1

Homework Statement


I did an experiment to measure the speed of sound(using two microphones and a hammer). I changed the distance between the two mics and calculated(using a fast timer) the time taken for the sound to reach from the start mic to the end mic. I made a graph(distance on x axis, time on y axis) on excel using my results and added a line of best fit. I need error bars and the uncertainty in the gradient. Also, I need to combine the uncertainty from the gradient with the random uncertainty, calibration and scale reading uncertainty(from meter stick). How can I do this?

Homework Equations

The Attempt at a Solution


I know the formula for random uncertainty and the Pythagoras-like formula for combining uncertainties. I just don't know how to combine all of this with the gradient uncertainty.
 
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  • #2
Banker said:
How can I do this?
In general: include uncorrelated uncertainties in the individual datapoints, make sure your fit takes those uncertainties into account (not sure if excel can do that). Correlated uncertainties need a different approach.
 
  • #3
@mfb Thanks for the reply I did a little experimenting and I combined the random uncertainty for each of my times with the digital reading uncertainty(using ∆w^2 = ∆x^2 + ∆y^2 + ∆z^2, x = random uncertainty, y = scale/digital reading uncertainty, z= calibration uncertainty ) and also did the same with my distances. I then plotted these in my excel graph as a custom error bar for each of my points. Is this the correct way to go? How would I go about finding the uncertainty of the gradient now, with the vertical and horizontal error bars in my graph too?
 
  • #4
Excel has a function for the uncertainty of parameters of linear functions, I don't know if you can also directly get them from a trend line, and I don't know if the uncertainties are taking into account properly (change them to see if the result changes).
 

What is gradient uncertainty and how does it differ from other uncertainties?

Gradient uncertainty is a type of uncertainty that arises from the measurement of a variable that changes continuously over a certain range. This is different from other uncertainties, such as random or systematic uncertainties, which are associated with discrete measurements or specific sources of error.

Why is it important to combine gradient uncertainty with other uncertainties?

Combining gradient uncertainty with other uncertainties allows for a more comprehensive understanding of the overall uncertainty in a measurement. This is especially important when the variable being measured is continuously changing, as it provides a more accurate representation of the true value.

What are some common methods for combining gradient uncertainty with other uncertainties?

There are several methods for combining gradient uncertainty with other uncertainties, including root-sum-square (RSS) method, maximum likelihood estimation (MLE), and Bayesian inference. Each method has its own advantages and should be chosen based on the specific scenario and data being analyzed.

How can one determine the amount of uncertainty associated with gradient measurements?

The amount of uncertainty associated with gradient measurements can be determined through a variety of methods, such as statistical analysis, simulations, or theoretical calculations. The specific method used will depend on the nature of the measurements and the available data.

Are there any limitations to combining gradient uncertainty with other uncertainties?

While combining gradient uncertainty with other uncertainties can provide a more accurate representation of overall uncertainty, it is important to note that this approach is not without limitations. For example, it may not be applicable in situations where the gradient is not well-defined or when there are significant correlations between different sources of uncertainty.

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