Problem with Linear Regression

Hi, everyone. I have a problem with Linear Regression. My code is :
knobSetting = [0 1 2 3 4 5]';
flowRate = [0.0 2.3 4.2 7.2 8.4 11.9]' ;
flowfit=fit(knobSetting,flowRate,'poly1');
Matlab gives me the following problem :
'fit' requires one of the following:
- Curve Fitting Toolbox
-Predictive Maintenance Toolbox
Error in Untitled2 (line 3)
flowfit=fit(knobSetting,flowRate,'poly1')

Antworten (4)

Stephan
Stephan am 25 Feb. 2019
Bearbeitet: Stephan am 25 Feb. 2019

0 Stimmen

Hi,
this problem can be solved easily by using mldivide. No additional toolboxes are needed:
knobSetting = [0 1 2 3 4 5]';
flowRate = [0.0 2.3 4.2 7.2 8.4 11.9]';
flowfit = knobSetting\flowRate
flowrate_calc = flowfit .* knobSetting
scatter(knobSetting,flowRate,'*r')
hold on
plot(knobSetting,flowrate_calc)
hold off
The result is a least squares fit of your data, where flowfit represents the slope of the linear function. Since your function goes through (0,0) this is the most simple case.
Best regards
Stephan
Star Strider
Star Strider am 25 Feb. 2019

0 Stimmen

A first-degree polynomial is a linear fit. You do not need any toolboxes to do a linear fit.
Try this:
knobSetting = [0 1 2 3 4 5]';
flowRate = [0.0 2.3 4.2 7.2 8.4 11.9]';
B = [knobSetting, ones(size(knobSetting))] \ flowRate;
regfit = [knobSetting, ones(size(knobSetting))] *B;
figure
plot(knobSetting, flowRate, 'p')
hold on
plot(knobSetting, regfit,' -r')
hold off
grid
text(0.6, 9, sprintf('flowRate = %.3f\\cdotknobSetting%.3f', B))
Image Analyst
Image Analyst am 25 Feb. 2019

0 Stimmen

You can simply use the built-in polyfit(x, y, 1) to get the equation of a line fitting your data, then use polyval() to get the fitted y-values at whatever x-locations you want:
% Create and plot existing data.
knobSetting = [0 1 2 3 4 5]';
flowRate = [0.0 2.3 4.2 7.2 8.4 11.9]' ;
coefficients = polyfit(knobSetting, flowRate, 1); % Get coefficients of the line.
plot(knobSetting, flowRate, 'bo');
hold on;
% Now get fitted values at the same Knob settings locations and plot them.
fittedValues = polyval(coefficients, knobSetting);
plot(knobSetting, fittedValues, 'r*-', 'MarkerSize', 10);
grid on;
legend('Actual', 'Fitted', 'Location', 'north');
0000 Screenshot.png
Claudia Orlando
Claudia Orlando am 11 Mär. 2019

0 Stimmen

Thank you all for your valuable advice.

Kategorien

Mehr zu Linear and Nonlinear Regression finden Sie in Hilfe-Center und File Exchange

Gefragt:

am 25 Feb. 2019

Beantwortet:

am 11 Mär. 2019

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by