![matlab polyfit matlab polyfit](https://dagarnahande.com/xvhxwt/VIYsiprig0HRnsl3kIbOQgHaEb.jpg)
^ 0 A = case 'line through origin' yeqn = coefs, x ) coefs ( 1 ) * x. ^ 0 A = case 'quadratic' yeqn = coefs, x ) coefs ( 1 ) * x. dat Force = Cantilever (:, 1 ) * 9.81 Displ = Cantilever (:, 2 ) * 2.54 / 100 %% Rename and create model data x = Force y = Displ xmodel = linspace ( min ( x ), max ( x ), 100 ) %% Define model equation and A matrix model = 'linear' switch model case 'linear' yeqn = coefs, x ) coefs ( 1 ) * x. %% Initialize the workspace clear format short e figure ( 1 ) clf %% Load and manipulate the data load Cantilever.
#Matlab polyfit code
In the example code below, there are several examples of general linear fits of one variable. Where the \(a_j\) are the coefficients of the fit and the \(\phi_j\) are the specific functions of the independent variable that make up the fit.
![matlab polyfit matlab polyfit](https://i.ytimg.com/vi/__TTPVc0tKM/maxresdefault.jpg)
![matlab polyfit matlab polyfit](https://www.mathworks.com/matlabcentral/mlc-downloads/downloads/submissions/23972/versions/22/previews/chebfun/examples/stats/html/LeastSquares_01.png)
General linear regression involves finding some set of coefficients for fits that can be written as: Independent and Model' ) legend ( 'Data', 'Estimates', 'Model', 'location', 'best' ) General Linear Regression xmodel, ymodel, 'k-' ) xlabel ( 'Independent Value' ) ylabel ( 'Dependent Value' ) title ( 'Dependent vs. ^ 2 ) % Compute the coefficient of determination r2 = ( St - Sr ) / St %% Generate plots plot ( x, y, 'ko'. ^ 2 ) % Compute sum of the squares of the estimate residuals Sr = sum (( y - yhat ). dat Force = Cantilever (:, 1 ) * 9.81 Displ = Cantilever (:, 2 ) * 2.54 / 100 %% Rename and create model data x = Force y = Displ xmodel = linspace ( min ( x ), max ( x ), 100 ) %% Determine the polynomial coefficients N = 1 P = polyfit ( x, y, N ) %% Generate estimates and model yhat = polyval ( P, x ) ymodel = polyval ( P, xmodel ) %% Calculate statistics % Compute sum of the squares of the data residuals St = sum (( y - mean ( y ) ).