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Matlab Working out an equation on MATLAB using co-ordinates

  1. Mar 8, 2017 #1
    I have a series of data points for X and Y points on a graph. The data is quite random and I am trying to work out a trend line so I can then form an equation for the line. How would I go about working out the equation for the data below using MATLAB?
    (0, 580)
    (6.7, 495)
    (13.4, 445)
    (18.7, 365)
    (22.8, 350)
    (27, 340)
    upload_2017-3-6_20-7-31-png.114188.png
    This is the data that has been plotted on Excel.
     
  2. jcsd
  3. Mar 8, 2017 #2

    DrClaude

    User Avatar

    Staff: Mentor

    Code (Matlab M):

    x = [0 6.7 13.4 18.7 22.8 27]
    y = [580 495 445 365 350 340]
     
     
  4. Mar 8, 2017 #3
    I managed to plot the points using that coding, but I am struggling with drawing a trend line and working out an equation.
     
  5. Mar 8, 2017 #4

    DrClaude

    User Avatar

    Staff: Mentor

    Did you look at the link I gave you in your other thread on how to do a fit using Matlab?
     
  6. Mar 8, 2017 #5
    Yes, I read through the link. However I was struggling to understand the MATLAB coding, hence why I have asked for further MATLAB coding help.
     
  7. Mar 18, 2017 #6
    Try this:

    Code (Text):

    % Initialization steps.
    clc;    % Clear the command window.
    close all;  % Close all figures (except those of imtool.)
    clear;  % Erase all existing variables. Or clearvars if you want.
    workspace;  % Make sure the workspace panel is showing.
    format long g;
    format compact;
    fontSize = 20;

    x = [0 6.7 13.4 18.7 22.8 27]
    y = [580 495 445 365 350 340]
    % Demo to illustrate how to use the polyfit routine to fit data to a polynomial
    % and to use polyval() to get estimated (fitted) data from the coefficients that polyfit() returns.
    % Demo first uses a quadratic fit via polyfit()


    %============= QUADRATIC FIT ===================================
    % Now we have sample, noisy y values that we will fit a curve through.

    % Plot the training set of data (our noisy y values).
    plot(x, y, 'ro', 'MarkerSize', 8, 'LineWidth', 2);
    grid on;
    xlabel('X', 'FontSize', fontSize);
    ylabel('Y', 'FontSize', fontSize);
    title('Linear Fit', 'FontSize', fontSize);

    % Enlarge figure to full screen.
    set(gcf, 'Units', 'Normalized', 'OuterPosition', [0 0 1 1]);
    % Give a name to the title bar.
    set(gcf, 'Name', 'Demo by ImageAnalyst', 'NumberTitle', 'Off')

    % Do the regression with polyfit.  Fit a quadratic curve through the noisy y values.
    coefficients = polyfit(x, y, 2)
    % Make fit.  It does NOT need to have the same
    % number of elements as your training set,
    % or the same range, though it could if you want.
    % Make 500 fitted samples going from the min x to the max x.
    xFit = linspace(min(x), max(x), 500);
    % Get the estimated values with polyval()
    yFit = polyval(coefficients, xFit);
    % Plot the fit
    hold on;
    plot(xFit, yFit, '.-', 'LineWidth', 2);
    legend('Training Set', 'Fit', 'Location', 'Northeast');
     
     
  8. Mar 19, 2017 #7
    That's great, thanks for your help! Is there anyway to show the equation for the 'fit' line?
     
  9. Mar 24, 2017 #8
    Saints-94, you can do

    fprintf('y = %f * x^2 + %f * x + %f\n', coefficients(1), coefficients(2), coefficients(3));
     
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