How Calculate R squared from a linear regress

Hey all I have this equation below and trying to figure out how to get the R^2.

I can't seem to understand what I am doing wrong.

x = 0:0.1:10;
n = 0;
noise = n*rand(1,length(x));

y = 2*x+1+noise; % y function 

b = regress(y(:),[ones(size(x(:))),x(:)]); % get the intercept and slope

figure();
plot(x,y,'d') %plot as diamonds
hline = refline(b(2),b(1)); % plot the liner regresson line
hline.Color = 'r'; % change linear regresson line to red 

I have looked at different ways in MATLAB but can't seem to understand.

Thank you.

Answer 1

[Star Strider]

They are hidden in the ‘stats’ output:

[b,~,~,~,stats] = regress(y(:),[ones(size(x(:))),x(:)]); % get the intercept and slope
Rsq = stats(1)
Rsq_p = stats(3)
Fstat = stats(2)
ErrVar = stats(4)

Even the documentation is not straightforward with disclosing which outputs are those it mentions. This is my best effort as to deciphering them. (I am certain that the first two are correct, based on my reading of the documentation.)