Try this:
D = load('NoLoadSpeed.mat');
T = load('Time.mat');
NLS = D.MotorSpeed_NoLoad; % Column Vector
t = T.Time; % Column Vector
pdv = 2:9; % Power & Denominator Vector
DM = t.^pdv./cumprod(pdv); % Design Matrix
BetaVct = DM \ NLS;
NLSfit = DM * BetaVct; % Fit Regression
figure
plot(t, NLS, '.')
hold on
plot(t, NLSfit, '-r')
hold off
grid
xlabel('Time')
ylabel('MotorSpeed_NoLoad')
legend('Data', 'Fit', 'Location','E')
The ‘pdv’ vector can only go to 9 before the design matrix loses rank. This produces 8 β coefficients (the ‘BetaVec’ vector).
