I have four arrays of data. Let's say these data are stored in a .xl file and the data arrays are a,b,c,d. I am trying to curve fit the data and generate a mathematical equation (it could be polynomial equation) such that:
d= function of (a,b,c) [where a, b, and c are independent parameters]
However, MATLAB only allows curve fitting for two independent variable which is a barrier for the Process.
Found some comments about Linear regression process in math work discussion thread. But I am not sure how to implement the process.
I am actually looking for a possible MATLAB code sample which can be modified to solve the above mentioned problem. Thanks in advance.
Yes, you can perform curve fitting with three independent variables in MATLAB. One approach is to use multiple linear regression, where you treat the three independent variables (a, b, and c) as predictor variables and the dependent variable (d) as the response variable.
Here's an example MATLAB code that demonstrates how to do this:
% Load your data from the .xlsx file (assuming the data is stored in sheet1)
data = xlsread('data_file.xlsx', 'Sheet1');
% Extract the columns for a, b, c, and d from the data
a = data(:, 1);
b = data(:, 2);
c = data(:, 3);
d = data(:, 4);
% Combine the predictor variables into a single matrix
X = [a, b, c];
% Perform multiple linear regression
mdl = fitlm(X, d);
% Display the regression results
disp(mdl)
% Extract the coefficients of the fitted equation
coefficients = mdl.Coefficients.Estimate;
% The coefficients will be in the order: Intercept, coefficient for a, coefficient for b, coefficient for c
intercept = coefficients(1);
coeff_a = coefficients(2);
coeff_b = coefficients(3);
coeff_c = coefficients(4);
% Create a function handle for the fitted equation: d = f(a, b, c)
fitted_equation = @(a, b, c) intercept + coeff_a*a + coeff_b*b + coeff_c*c;
% Now you can use 'fitted_equation' to calculate d for any values of a, b, and c.
The fitlm function performs the multiple linear regression, and the coefficients of the fitted equation are obtained from the Coefficients. Estimate the property of the model. Once you have the coefficients, you can create a function handle to represent the fitted equation and use it to calculate `d` for any combination of `a`, `b`, and `c`.
Keep in mind that if your data suggest a non-linear relationship between `d`, `a`, `b`, and `c`, you may need to consider other types of regression models, such as polynomial regression or other non-linear regression techniques. However, the code provided above demonstrates the process of multiple linear regression for your data.
Yes, you can curve fit in MATLAB for three independent variables using 'fittype' and 'fit' functions. It's like finding the perfect match for your data - just like finding a soulmate for your curves! So, get ready to romance the numbers and let MATLAB do the matchmaking! Happy fitting! 😄
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