For a simple linear regression with a single predictor variable, you can plot the regression line using
mdl = fitlm(tbl,'Mass~Length');
coefs = mdl.Coefficients.Estimate; % 2x1 [intercept; slope]
plot(length, mass, 'o')
hold on
% Plot line
refline(coefs(2),coefs(1));
or, more simply,
cla()
plot(length, mass, 'o')
% Plot line
lsline
lsline can also be used to plot the simple linear regression of each group if the groups are plotted as separate objects.
or
% Get coefficients using polyfit
nanIdx = isnan(length) | isnan(mass);
coefs = polyfit(length(~nanIdx), mass(~nanIdx), 1);
% Plot line
refline(coefs);
If the model includes multiple predictors, you can use plot(mdl) or plotAdded(mdl) (same result) to create a partial regression leverage plot (aka "added variable plot"). If you want to specify the coefficients to be used in the regression line, use plotAdded(mdl,coef).
If you want to plot the regression line given a new set of x-values, use ypred = predict(mdl,Xnew) to compute the new y values and then plot the results using plot(Xnew, ypred).
Or perhaps you want to plot a range of possible regression lines while holding all but one term constant. That's demonstrated here in a non-linear regression (fitnlm) demo but the same logic can be applied to the a fitlm model.
To explore your model in various visualizations developed for the fitlm output, see this workflow in the documentation.
