create multivariable regression function in matlab

To create a multivariable regression function in MATLAB, you can use the regress function.

The general syntax of the regress function is:

main.m
[b, bint, r, rint, stats] = regress(y, X)
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where:

• y is a column vector representing the response variable.
• X is a matrix where each column represents a predictor variable.

The output values are:

• b is a column vector of estimates for the coefficients in the linear regression model.
• bint is a matrix where each row represents the lower and upper 95% confidence interval bounds for the corresponding coefficient estimate in b.
• r is a column vector of residuals (the differences between the observed and predicted values).
• rint is a matrix where each row represents the lower and upper 95% confidence interval bounds for the corresponding residual in r.
• stats is a vector of statistics that include the R-squared statistic, the F-statistic, and the p-value for the full model.

Here's an example illustrating how to use the regress function for multivariable regression:

main.m
% Create some example data
y = [1; 2; 3; 4; 5]; % Response variable
X = [1, 2; 2, 4; 3, 6; 4, 8; 5, 10]; % Predictor variables

% Perform the multivariable regression
[b, bint, r, rint, stats] = regress(y, X);

% Display the results
disp('Coefficients (b):');
disp(b);
disp('Confidence Intervals for Coefficients (bint):');
disp(bint);
disp('Residuals (r):');
disp(r);
disp('Confidence Intervals for Residuals (rint):');
disp(rint);
disp('Statistics (stats):');
disp(stats);
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Make sure the y and X variables are correctly defined according to your data.

Note that the regress function assumes that the first column of X is a column of ones, representing the intercept term. If you don't want an intercept term, you can remove the first column of X.

Remember to check the documentation for additional details and options: https://www.mathworks.com/help/stats/regress.html