Curve Fit Q: Balancing 1+m1*log(x) & 4.5+m2*log(x)

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In summary, the speaker is trying to fit a third curve to balance two existing curves, with the goal of minimizing error. They are having difficulty getting an initial reduced slope when using a specific equation in Excel, and believe that adding a negative exponential may result in a curve similar to the third curve they are trying to fit. They are seeking suggestions on how to achieve this balance and minimize error.
  • #1
I have two curves 1+m1*log(x) and 4.5+m2*log(x) [please see the attached picture]. I want to fit a third curve (which might of any equation), which should actually 'balance' the first two curves, so that the error is minimum. My problem is I am not able to get a initial reduced slope in the third curve when I try to fit with an eqn (in excel) of form a+b*log(x). I have a feeling like adding a negative exponential or something similar to that with eqn of form a+b*log(x) we might arrive at a curve similar to curve 3 [please see the attached picture]. Any suggestions? Thanks guys.
 

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  • #2
mikewinifred said:
I have two curves 1+m1*log(x) and 4.5+m2*log(x) [please see the attached picture]. I want to fit a third curve (which might of any equation), which should actually 'balance' the first two curves, so that the error is minimum. My problem is I am not able to get a initial reduced slope in the third curve when I try to fit with an eqn (in excel) of form a+b*log(x). I have a feeling like adding a negative exponential or something similar to that with eqn of form a+b*log(x) we might arrive at a curve similar to curve 3 [please see the attached picture]. Any suggestions? Thanks guys.
What do you mean by "balance" the first two curves? And what error are you talking about?
 

What is curve fitting?

Curve fitting is the process of finding a mathematical function that best fits a set of data points. It involves adjusting the parameters of the function to minimize the difference between the predicted values and the actual data.

What is Q: Balancing 1+m1*log(x) & 4.5+m2*log(x)?

Q: Balancing 1+m1*log(x) & 4.5+m2*log(x) is a specific type of curve fitting where the function being fit is a combination of logarithmic and linear terms. The goal is to find the values of m1 and m2 that will result in the best fitting curve.

What does the 1 and 4.5 in the equation represent?

The 1 and 4.5 in the equation represent the intercepts of the logarithmic and linear terms, respectively. They determine the starting points of the curves on the x-axis.

What does the m1 and m2 in the equation represent?

The m1 and m2 in the equation represent the slope of the logarithmic and linear terms, respectively. They determine the steepness of the curves.

What is the significance of the log(x) term in the equation?

The log(x) term in the equation represents a logarithmic relationship between the independent variable (x) and the dependent variable (y). This type of relationship is commonly used in curve fitting to account for data that increases or decreases exponentially.

Suggested for: Curve Fit Q: Balancing 1+m1*log(x) & 4.5+m2*log(x)

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