Approximating Coefficients for Line Approximation from Experimental Data

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In summary, the speaker has a situation where they can approximate a line with a function and use experimental data to find values for the variables. They need to find coefficients for the function and are wondering how to do so with limited data. The suggested method is to use the least squares approximation, which involves creating a matrix and solving a linear system to find the coefficients that will minimize the sum of squares of the differences between the data points and the function. The speaker thanks the other person for the suggestion and will try to understand and use this method.
  • #1
Zurtex
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I've got a situation where I can approximate a line by the function:

[tex]y = a_0 + a_1 \sin x + a_2 \sin 2x + b_1 \cos x + b_2 \cos 2x[/tex]

From experimental data I will be able to find certain values for x and y, namely yn and xn for some value of n. Now I can easily write a computer program which could work out the co-efficients in this problem if I have 5 values for y and 5 values for x. However, being a particle situation and wanting as many results as possible or perhaps not being able to gain that many results due to constraints, how would I be able to approximate these co-efficient please?
 
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  • #2
How about least squares approximation?

Let the ith row of an n by 5 matrix [tex]M[/tex] be given by [tex][ 1\ \sin(x_i)\ \sin(2x_i)\ \cos(x_i)\ \cos(2x_i)][/tex], and let [tex]Y=[y_1,\ldots,y_n]^T[/tex].

Then coefficients that will minimize the sum of the squares of the differences of your data points to [tex]y = a_0 + a_1 \sin x + a_2 \sin 2x + b_1 \cos x + b_2 \cos 2x[/tex] will be any column [itex]A=[a_0,a_1,a_2,b_1,b_2]^T[/itex] which satisfies the linear system [itex](M^{T}M)A=M^{T}Y[/itex]
 
  • #3
Thanks I haven't seen that method yet (well I've researched it but not been able to make sense of it). Thanks a lot, just need to try and make sense of it, but I think I can :smile:
 

1. What is meant by "approximating co-efficents"?

Approximating co-efficents refers to the process of estimating numerical values for the coefficients in a mathematical equation or model. This is typically done when the exact values of the coefficients are either unknown or difficult to calculate.

2. Why is approximating co-efficents important in scientific research?

Approximating co-efficents allows for the simplification of complex mathematical models, making them easier to analyze and interpret. It also enables scientists to make predictions and draw conclusions based on available data, even if the exact values of the coefficients are not known.

3. How are co-efficents approximated?

Co-efficents can be approximated using a variety of methods, such as linear regression, least squares fitting, or maximum likelihood estimation. These methods involve finding the best fit for the data and determining the most likely values for the coefficients based on the available information.

4. What are the limitations of approximating co-efficents?

One limitation of approximating co-efficents is that it can introduce errors into the analysis, as the estimated values may not be as accurate as the exact values. Additionally, approximating co-efficents may not be appropriate for all types of data or mathematical models.

5. Can approximating co-efficents be used in all scientific fields?

Yes, approximating co-efficents can be used in various scientific fields, including physics, chemistry, biology, and social sciences. It is a common practice in data analysis and modeling, and can be applied to a wide range of research topics and questions.

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