Help Me Find Df & k Values Using Non-Linear Regression

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SUMMARY

The discussion focuses on performing non-linear regression to determine the values of Df and k using a specified equation and dataset. The equation provided involves parameters such as Mo, Ms, Pi, r, and n, with a dataset containing time (t) and corresponding measurements (m). Users are encouraged to utilize software tools capable of handling non-linear regression to fit the data effectively. The specific equation and parameters are critical for accurate modeling and analysis.

PREREQUISITES
  • Understanding of non-linear regression techniques
  • Familiarity with mathematical modeling and parameter estimation
  • Proficiency in using statistical software such as R or Python's SciPy library
  • Knowledge of the specific equation structure and its components
NEXT STEPS
  • Explore R's nls() function for non-linear least squares fitting
  • Learn how to use Python's SciPy library for curve fitting with curve_fit()
  • Investigate the use of MATLAB's fit function for non-linear regression
  • Study the principles of parameter estimation in non-linear models
USEFUL FOR

Researchers, data analysts, and engineers involved in mathematical modeling and statistical analysis, particularly those working with non-linear regression in scientific and engineering contexts.

ayildirim10
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Dear everybody,
I wave an equation with datas and want to regress in non linear form with plot.
Would you help me to solve the equation bellow by any softwares, please?
The data, the equation and parameters are given bellow. I want to fit the data to non-linear equation to find Df and k values.
I just need help.
Your sincerely,,

M=Mo + (Mo - Ms)*Pi*8*r*Ms*
a [Sum[((k*r^2 + Pi^2*a n^2)*k*r^2*t -
Pi^2*r^2*a n^2 - (Pi^2*r^2*a) n^2*
Exp[-t*(k*r^2 + a*Pi^2 n^2)/r^2])/(k*r^2 + n^2 a*Pi^2)^2]


n=1000
ms=146.5
mo=11.58
r=0.003975

t m
0 11.58
30 72.61
60 97.89
90 106.05
120 109.63
150 117.02
180 119.57
210 125.99
240 129.56
270 132.85
 
Last edited by a moderator:
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Your equation looks incomplete
 

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