What is the uncertainty in the derivative of a function?

In summary, the author is trying to find the velocity-time function of an object under constant acceleration using software, but wants to be able to generate more uncertainty in the result. There are several ways to do this, depending on the accuracy of the software and the accuracy of the data.
  • #1
CSA
15
0
Hey,

Homework Statement



I was working on a kinematics experiment using Tracker to do a video analysis. I obtained a graph of displacement against time for the body under constant acceleration and the software also gives me the rms error between the parabolic trend line and the data points representing the position of the object over time.

However, I actually want the velocity-time function of the object but I do not want to rely on the software's algorithms to determine it. As such, I plan to find the derivative of the displacement-time trend line by hand however, is there a way to calculate the uncertainty in the derivative using the rms value of the original function?

Homework Equations



s(t) = At^2 + Bt + c
v = s'(t) = 2At + B
 
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  • #2
There are several approaches which I view as complementary. Which error estimate I favor depends on how well the frame rate and pixel resolution match the original experiment and whether more uncertainty is likely to be introduced by 1) a relatively slow frame rate (velocity changing significantly between frame intervals) 2) a relatively coarse pixels (movement of 100 pixels per frame is better than 8 pixels per frame) 3) motion not being all in the same plane perpendicular to the line of sight 4) optical effects 5) the reasonableness of the models used in determining the displacement function.

Assigning the displacement to be a quadratic assumes a constant force, which is usually not true in most real experiments. It can only lead to a linear change in velocity (constant acceleration). But it does allow a rough estimate of uncertainties in a couple of ways: 1) propagate the error from the uncertainties in the fitting parameters A, B, and C. 2) repeat the analysis with a higher order polynomial and compute the resulting differences in velocity estimates.
 
  • #3
Alright, thanks so much for the help!
 

FAQ: What is the uncertainty in the derivative of a function?

1. What is uncertainty in the derivative of a function?

Uncertainty in the derivative of a function refers to the amount of error or imprecision in the calculated value of the derivative. It is a measure of how much the derivative may vary from its true value due to limitations in the data or measurement process.

2. How is uncertainty in the derivative calculated?

The uncertainty in the derivative is typically calculated using the standard error propagation formula, which takes into account the uncertainties in the measured values used to calculate the derivative. This formula uses the partial derivatives of the function with respect to each variable, as well as the uncertainties in those variables.

3. What factors contribute to uncertainty in the derivative?

There are several factors that can contribute to uncertainty in the derivative of a function, including measurement error, rounding errors, limitations in data collection or processing, and inherent variability in the function itself. It is important to consider and minimize these factors when calculating and interpreting the uncertainty in a derivative.

4. How does uncertainty in the derivative affect the overall accuracy of a function?

The uncertainty in the derivative can have a significant impact on the overall accuracy of a function, as it can affect the slope and curvature of the function at different points. A larger uncertainty in the derivative can result in a less precise estimation of the function's behavior and may lead to incorrect conclusions or predictions.

5. Can uncertainty in the derivative be reduced?

While it is not possible to completely eliminate uncertainty in the derivative, it can be reduced by improving the accuracy and precision of the data used to calculate the derivative, as well as minimizing sources of error. Additionally, using more advanced statistical methods and techniques can also help to reduce the uncertainty in the derivative.

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