Regression Analysis for a Gamma function

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Discussion Overview

The discussion revolves around the application of regression analysis to a Gamma function, specifically in the context of modeling flow data related to a flooding situation. Participants explore methods for transforming the Gamma function into a suitable form for regression analysis, considering both linear and non-linear approaches.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Experimental/applied

Main Points Raised

  • One participant describes a Gamma function I(t) = P*(t^s)*(e^(-ft)) and seeks to modify their regression program to include this function, expressing concern about handling the term (-ft).
  • The same participant wonders if they can solve for the constants P, s, and f using matrices, given 18 data points, despite having more rows than columns in their transformed equation.
  • Another participant suggests running a regression of the form y(t) = a + b1 x1(t) + b2 x2(t) + error, where y = log I, x1 = log t, and x2 = t, proposing that the parameters can be defined as a = Log P, b1 = s, and b2 = -f.
  • A later reply acknowledges the suggestion and notes the shift from one independent variable to two in the transformed equation.

Areas of Agreement / Disagreement

Participants appear to be exploring different approaches to the regression analysis without reaching a consensus on the best method. Multiple views on how to handle the regression and the transformation of variables are present.

Contextual Notes

Participants have not resolved the implications of having more data points than parameters in the regression model, nor have they clarified the assumptions behind their proposed methods.

chicopee
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[SOLVED] Regression Analysis for a Gamma function

My regression analysis program that I developed in BASICS back in the 1980's applies for half a dozen linear equations some of which are transormed into log forms. I would like to modify my program to include this Gamma function: I(t)=P*(t^s)*(e^(-ft)) which I can transform into this equivalent non- linear log form: Ln I(t)=Ln P + s*Ln t + (-ft); P,s and f are constants; t if for time; I(t) has for units cu.ft/sec or cu.m./sec. Is there any way to take care of the term (-ft). I got 18 data points available for this regression .

I am not looking forward to trial and error to determine the constants P,s and f.

Here is another thought. Since I have 18 data points (flow vs time), can I solve theses constants with matrices using this transformation: Ln I(t)=Ln P + s*Ln t + (-ft) eventho I would have 18 rows and and only 4 columns?
 
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Note, my request is not homework. This is a real situation involving a flooding situation arising from tropical storm Tammy back in 2005.
 
Why not run the regression y(t) = a + b1 x1(t) + b2 x2(t) + error, where y = log I, x1 = log t, x2 = t, and the parameters are a = Log P, b1 = s, b2 = -f?
 
Yeah, I see what you mean. Instead of one independent variable, I'll two in that transformed equation. Thx.
 

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