Do you have the Statistics Toolbox?
One way:
load carsmall;
X = ones(length(Horsepower),2);
X(:,2) = Horsepower;
Y = Acceleration;
[b,bint] = regress(Y,X);
x = min(Horsepower):0.01:max(Horsepower);
yfit = b(1)+b(2)*x;
plot(Horsepower,Y,'k*')
hold on;
ylower = bint(1,1)+ bint(2,1)*x;
yupper = bint(1,2)+bint(2,2)*x;
plot(x,yfit,'r','linewidth',2)
plot(x,ylower,'b-.')
plot(x,yupper,'b-.')
Another way using LinearModel.fit
mdl = LinearModel.fit(Horsepower,Y);
ci = coefCI(mdl);
Note that ci is the same matrix as bint. The nice thing about LinearModel.fit is that there is a plot() method that will give you the plot with the confidence intervals
plot(mdl)
