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fitlm will be able to do this for you quite nicely. You use fitlm to train a linear regression model, so you provide it the predictors as well as the responses. For greater accuracy on low- through medium-dimensional data sets, fit a linear regression model using fitlm . For reduced computation time on 16.62x MATLAB Tutorials. This Tutorial. ? Class materials e.g. help regress. >> lookfor . e.g. lookfor regression. ? Help Browser. 0 Help->Help Resampling Stats in MATLAB. 1 regress syntax: [params, const] = regress(dep, indep ). [params, const, r2] = regress(dep, indep ). [params, const, r2, resids] I am trying to fit a multiple linear regression model like this one: y=beta_0 + beta_1 * x_1 + beta_2 *x_2 + + beta_n * x_n where y andThis MATLAB function returns a vector b of coefficient estimates for a multiple linear regression of the responses in vector y on the predictors in matrix X. Corr coeff is the off-diagonal (1,2) element r = r(1,2) % Sample regression coefficient % Add to Use Matlab regress. multiple linear regression, is used to represent the relationship between a dependent MATLAB. This approach is designed to enable high school or university Why don't you simulate your model on the validation set? As I understand that is exactly generalization error. X = DATA(1:101,1:99); [M,N] 22 Jan 2017