## Main.MatlabDataRegression History

August 13, 2020, at 01:03 PM by 136.36.211.159 -
There is additional information on regression in the [[https://apmonitor.github.io/data_science|Data Science online course]].
June 21, 2020, at 04:11 AM by 136.36.211.159 -
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March 20, 2018, at 12:51 PM by 10.37.35.33 -
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March 20, 2018, at 12:50 PM by 10.37.35.33 -
(:toggle hide fmincon_minimize button show="MATLAB fmincon Solution":)
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March 20, 2018, at 12:49 PM by 10.37.35.33 -
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Both regression tutorials can also be completed with [[Main/ExcelDataRegression|Excel]] and [[Main/PythonDataRegression|Python]]. Click on the appropriate link for additional information.

!!!! Excel and Python Solutions

The regression tutorials can also be completed with [[Main/ExcelDataRegression|Excel]] and [[Main/PythonDataRegression|Python]]. Click on the appropriate link for additional information.
March 20, 2018, at 12:48 PM by 10.37.35.33 -
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March 20, 2018, at 12:46 PM by 10.37.35.33 -

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March 20, 2018, at 12:40 PM by 10.37.35.33 -
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A frequent activity for scientists and engineers is to develop correlations from data. By importing the data into MATLAB, data analysis such as statistics, trending, or calculations can be made to synthesize the information into relevant and actionable information. This tutorial demonstrates how to create a linear or polynomial functions that best approximate the data trend, plot the results, and perform a basic statistical analysis. A script file of the MATLAB source code with sample data is below.
to:
A frequent activity for scientists and engineers is to develop correlations from data. By importing the data into MATLAB, data analysis such as statistics, trending, or calculations can be made to synthesize the information into relevant and actionable information. This tutorial demonstrates how to create a linear, polynomial, or nonlinear function that best approximates the data trend and how to analyze the solution. Script files of the MATLAB source code with sample data are below.
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!!!!Nonlinear Regression
to:
!!!!Nonlinear Regression with APM MATLAB

!!!!Nonlinear Regression with MATLAB fmincon

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(:source lang=matlab:)
% clear session, close plots, clear screen
clear all; close all; clc
% data for regression
xm = [18.3447,79.86538,85.09788,10.5211,44.4556, ...
69.567,8.960,86.197,66.857,16.875, ...
52.2697,93.917,24.35,5.118,25.126, ...
34.037,61.4445,42.704,39.531,29.988];
ym = [5.072,7.1588,7.263,4.255,6.282, ...
6.9118,4.044,7.2595,6.898,4.8744, ...
6.5179,7.3434,5.4316,3.38,5.464, ...
5.90,6.80,6.193,6.070,5.737];
% initial parameter guess
p0 = [1,0.5,1];
% define prediction function
yp = @(p) p(1) + p(2)./xm + p(3).*log(xm);
% define objective function (scaled sum of squared errors)
objective = @(p) sum(((yp(p)-ym)./ym).^2);
disp(['Initial Objective: ' num2str(objective(p0))])

% optimize with fmincon
% = fmincon(FUN,X0,A,B,Aeq,Beq,LB,UB,NONLCON,OPTIONS)
A = [];
b = [];
Aeq = [];
beq = [];
% bounds
lb = []; % ones(3)*0.2;
ub = []; % ones(3)*1.5;
popt = fmincon(objective,p0,A,b,Aeq,beq,lb,ub);

% print results
disp(['Final Objective: ' num2str(objective(popt))])
disp(['Optimal parameters: ' num2str(popt)])

% plot results
plot(xm,ym,'ro')
hold on
plot(xm,yp(p0),'bx')
plot(xm,yp(popt),'gs')
legend('measured','initial predicted','optimal predicted')
ylabel('y')
xlabel('x')
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August 22, 2015, at 10:34 PM by 174.148.220.158 -
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August 22, 2015, at 09:45 PM by 174.148.220.158 -
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August 22, 2015, at 03:46 AM by 174.148.85.243 -

* [[Attach:MATLAB_nonlinear_regression.zip|MATLAB Nonlinear Regression Example Source Code]]
August 22, 2015, at 03:44 AM by 174.148.85.243 -
!!!!Linear and Polynomial Regression
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This regression tutorial can also be completed with [[Main/ExcelDataRegression|Excel]] and [[Main/PythonDataRegression|Python]]. Click on the appropriate link for additional information.
to:
!!!!Nonlinear Regression

(:html:)
<iframe width="560" height="315" src="https://www.youtube.com/embed/CejquWoc210" frameborder="0" allowfullscreen></iframe>
(:htmlend:)

Both regression tutorials
can also be completed with [[Main/ExcelDataRegression|Excel]] and [[Main/PythonDataRegression|Python]]. Click on the appropriate link for additional information.
August 21, 2015, at 01:53 AM by 10.10.146.39 -
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August 21, 2015, at 01:30 AM by 10.10.146.39 -
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!!!!Python Data Regression
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!!!!MATLAB Data Regression
August 21, 2015, at 01:17 AM by 10.10.146.39 -
(:title Data Regression with MATLAB:)
(:keywords data regression, MATLAB, nonlinear, polynomial, linear regression, university course:)
(:description Data Regression with MATLAB - Problem-Solving Techniques for Chemical Engineers at Brigham Young University:)

!!!!Python Data Regression

A frequent activity for scientists and engineers is to develop correlations from data. By importing the data into MATLAB, data analysis such as statistics, trending, or calculations can be made to synthesize the information into relevant and actionable information. This tutorial demonstrates how to create a linear or polynomial functions that best approximate the data trend, plot the results, and perform a basic statistical analysis. A script file of the MATLAB source code with sample data is below.

* [[Attach:MATLAB_data_regression.zip|MATLAB Data Regression Example Source Code]]

(:html:)
<iframe width="560" height="315" src="https://www.youtube.com/embed/ro5ftxuD6is" frameborder="0" allowfullscreen></iframe>
(:htmlend:)

This regression tutorial can also be completed with [[Main/ExcelDataRegression|Excel]] and [[Main/PythonDataRegression|Python]]. Click on the appropriate link for additional information.

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(:html:)
<script type="text/javascript">
/* * * CONFIGURATION VARIABLES: EDIT BEFORE PASTING INTO YOUR WEBPAGE * * */
var disqus_shortname = 'apmonitor'; // required: replace example with your forum shortname

/* * * DON'T EDIT BELOW THIS LINE * * */
(function() {
var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true;
dsq.src = 'https://' + disqus_shortname + '.disqus.com/embed.js';