One approach —
x = linspace(0.1, 85, 50); % Create Data
y = atanh((x-mean(x))/max(x)*2)/4+0.5 + randn(size(x))*0.01; % Create Data
X = [x(:).^3 x(:).^2 x(:) ones(size(x(:)))]; % Design Matrix
[B,Bint,R,Rint,Stats] = regress(y(:), X);
ParameterStats = table(Bint(:,1), B, Bint(:,2), 'VariableNames',{'Lower 95% CI','Parameter Value','Upper 95% CI'}, 'RowNames',{'x^3','x^2','x','Const'})
Rsq = Stats(1)
F_p = [Stats(2:3)]
ErrorVar_Est = Stats(4)
figure
plot(x, y, '+b')
hold on
plot(x, X*B, '-r')
hold off
grid
Experiment with the actual data.
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