Regression Analysis: Finding Optimal Parameters for Non-Linear Functions

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The discussion revolves around determining the parameter 'c' in the sine regression function y=asin(b(x-c))+d without graphing the data. It is noted that while most parameters can be calculated mathematically, 'c' typically requires non-linear optimization methods, such as least-squares, to minimize a cost function. The conversation highlights that graphing is not essential for parameter determination but can aid in model selection for simple datasets. Additionally, there is interest in whether a general formula exists for various regression types, emphasizing the potential for hand calculations using statistical concepts like variance. Understanding cost functions and optimization is crucial for effectively applying these regression techniques.
JoeTarmet
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Sorry if this is somewhat elementary but the regression form of the sine function with data provided is y=asin(b(x-c))+d

As far as I know, all of the variables except c can be determined mathematically. My question is this, using calculus or any other method, is there a way to determine c without graphing the data?


As a follow up, is there a general formula or procedure that applies to all types of Regression (linear, Cubic, Sinusoidal, etc.)? I want to know this because I believe it is possible using statistical concepts like variance and I want to find out how many of these can be done completely by hand without graphing.
 
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Your question is very confusing. Graphing isn't used to determine regression parameters, though it can be used to select general models for some very simple datasets. Generally, the parameters would be selected by minimizing some cost function (usually through least-squares), which in your case would require using some sort of non-linear optimization method, since your function is non-linear in its parameters.
 
Number Nine said:
Generally, the parameters would be selected by minimizing some cost function (usually through least-squares), which in your case would require using some sort of non-linear optimization method, since your function is non-linear in its parameters.

Please explain this further, I am interested in what you are saying about cost functions and optimization, but I don't understand these terms.
 
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