Fitted error vs error of original fit

I am asked to compare the fitted error to the error of original fit of a set of data labeled data_valid_fit. Using a given equation I calculated the fitting error as below:

% The data estimated on the validation set with r and y0
valid_data_fit = y0.*exp(r*tvalid)

% Computing fitting error
e = 1/length(t)*sqrt(sum((valid_data_fit - datavalid).^2))

If my function is y= y0*exp(r*t) and I calculated datafit using that function and:

y0= 149.4515
r= 0.0330
t = [ 0, 13, 36, 46, 61, 64, 70, 75, 78]. 

How can I calculate the error of original fit?

Answer 1

[Ayush Gupta]

There seems to be a confusion between error of original fit which is calculated previously whereas fitted error of a fit is the model parameter which is known as MSE (Mean Squared Error). For examples on how to calculate the MSE, read the documentation here.