The 12a release of Statistics Toolbox has some very nice new capabilities for regression analysis.

X = linspace(1,100, 50);

X = X';

Y = 5*X + 50;

Y = Y + 20*randn(50,1);

myFit = LinearModel.fit(X,Y)

The object that is generated by LinearModel includes the Standard Error as part of the default display.

myFit = LinearModel.fit(X,Y)

myFit =

Linear regression model:

y ~ 1 + x1

Estimated Coefficients:

Estimate SE tStat pValue

(Intercept) 63.499 7.0973 8.9469 8.4899e-12

x1 4.8452 0.12171 39.809 2.0192e-38

Number of observations: 50, Error degrees of freedom: 48

Root Mean Squared Error: 25.1

R-squared: 0.971, Adjusted R-Squared 0.97

F-statistic vs. constant model: 1.58e+03, p-value = 2.02e-38

Please note:

This same information is available in earlier versions of the product. For example, the second output from regress is "bint" which are the confidence intervals for the regression coefficients.

However, I think that the display capabilities for the LinearModel objects are a big improvement over what came before.