Best Method for Calculating % Error Between Two Curves?

In summary, the conversation is about finding the best method to calculate the distance between two curves. The proposed method involves taking the square root of the integral of the difference between the two curves divided by the line integral of one of the curves. However, it is unclear what is meant by "% error between two curves" and the conversation shifts to discussing the distance between the curves instead. The original method may not be valid if the curves do not share the same concavity. The conversation ends with the question of whether anyone has any ideas on how to find the average distance between the two curves.
  • #1
member 428835
Hi PF!

Can anyone tell me what you think the best method is to calculate the % error between two curves?

My thoughts were to take $$100\frac{ \sqrt{\int_a^b (f-g)^2 \, dx}}{\int_s f \, ds}$$

where the integral in the denominator is a line integral. What are your thoughts? Thanks for looking!
 
Physics news on Phys.org
  • #2
It is impossible to answer this question unless you specify what you mean by "% error between two curves".
 
  • Like
Likes jim mcnamara
  • #3
I'm trying to find a good way to see how different two curves are from each other given a domain. I don't want the measure to be domain dependent, where perhaps two curves over a large domain are in fact very close to the same but since the domain is large it may appear as if both graphs are less unrelated than they actually are.

Does this clarify or am I still being too vague?
 
  • #4
Are the data from a normally distributed population? Or 'what are you testing?' -- state your hypothesis and then explain the data sources. As is I cannot help either.
 
  • #5
I have two curves, call them ##f(x,t)## and ##g(x,t)##. Given some point in time, call it ##t_0##, I want to see how "close" ##f(x,t_0)## and ##g(x,t_0)## are. Whatever scheme is proposed, I am hoping that scheme will account for a growing domain, because as time increases the ##x## domain expands.

Is this clear, or still poorly described?
 
  • #6
joshmccraney said:
I have two curves, call them ##f(x,t)## and ##g(x,t)##. Given some point in time, call it ##t_0##, I want to see how "close" ##f(x,t_0)## and ##g(x,t_0)## are. Whatever scheme is proposed, I am hoping that scheme will account for a growing domain, because as time increases the ##x## domain expands.

Is this clear, or still poorly described?
No, not clear. The graphs of z = f(x, t) and z = g(x, t) are not curves, but are surfaces in three dimensions. If you fix t = ##t_0## you get traces (which are curves) on the two surfaces. Does that agree with what you're thinking?

I am hoping that scheme will account for a growing domain
I don't understand this part. The domains of f and g are subsets of the plane. How are the domains supposed to be changing?
 
  • Like
Likes jim mcnamara
  • #7
Mark44 said:
No, not clear. The graphs of z = f(x, t) and z = g(x, t) are not curves, but are surfaces in three dimensions. If you fix t = ##t_0## you get traces (which are curves) on the two surfaces. Does that agree with what you're thinking?
Sorry, this is exactly what I was thinking, but obviously didn't do a great job wording it.

Mark44 said:
I don't understand this part. The domains of f and g are subsets of the plane. How are the domains supposed to be changing?
Sorry again. So as you consider different traces for different values of ##t## the domain in the ##z-x## plane apparently changes.
 
  • #8
I'm starting to understand your question, which involves level curves or traces on two surfaces. Instead of talking about the "% error" it seems that what you want is the distance between the two curves. At least that's what I infer from the ##(f - g)^2## business in the integral of post #1.It looks sort of like you're trying to find the average distance between the two curves.
 
  • #9
Yes, the average distance sounds like what I'm looking for. Any ideas? After talking to a friend, I believe the proposition I first posed would only be valid if the traces for ##f## and ##g## share the same concavity (otherwise the line integral in the denominator could be very large).
 

FAQ: Best Method for Calculating % Error Between Two Curves?

What is the purpose of calculating % error between two curves?

The purpose of calculating % error between two curves is to determine the accuracy of one curve compared to another. This can be useful in a variety of scientific fields, such as physics, chemistry, and engineering, where precise measurements and calculations are necessary.

What is the formula for calculating % error between two curves?

The formula for calculating % error between two curves is:
% error = |(experimental value - theoretical value)| / theoretical value * 100%.
This formula takes the absolute value of the difference between the two values, divides it by the theoretical value, and multiplies it by 100 to express the error as a percentage.

What are the limitations of using % error to compare two curves?

One limitation of using % error to compare two curves is that it does not take into account the direction of the error. For example, if the experimental value is higher than the theoretical value, the % error will be the same as if the experimental value is lower. Additionally, % error does not consider any other sources of error in the measurement process, such as human error or equipment limitations.

How can % error be minimized when calculating between two curves?

To minimize % error when calculating between two curves, it is important to ensure accurate and precise measurements are taken. This can be achieved by using high-quality equipment and techniques, taking multiple measurements, and averaging the results. Additionally, it is important to carefully consider any potential sources of error and try to minimize their impact on the measurements.

Can % error be negative?

Yes, % error can be negative. This occurs when the experimental value is lower than the theoretical value, resulting in a negative value when the formula is calculated. However, it is important to note that % error should always be reported as a positive value, so the absolute value should be taken when presenting the final result.

Similar threads

Replies
6
Views
2K
Replies
2
Views
2K
Replies
12
Views
2K
Replies
13
Views
2K
Replies
20
Views
3K
Back
Top