n this exercise, you will be computing the Root Mean Square Error (RMSE) between the measured and theoretical displacement from a spring stiffhess test. RMSE is used as a al measure of the error between two vectors, and is calculated by taking the square root of the mean (average) value for error squared. E can be computed by determining the RMS of an error vector. In this case, the error vector is the difference between the measured displacement vector and the theoretical to be calculated by you. splacement vector (recall x = F/k). Note that RMS was discussed in the previous non-assessed exercise (Problem 2 Week 10). But in contrast to that exercise, the error vector now nputs: ata this is a matrix containing data from a spring stiffness test. Column 1 is the force data and column 2 is the displacement data. Your code should work regardless of how many s data has. this is an estimate for the spring stiffness. utputs: SE - The root mean square error between the data and the fit line based on k. Function Template: unction RMSE = rmse(data, k) INSERT CODE Test Example ou can try testing your function on the data matrix you were provided in Exercise 1 of Week 10 (this week) Submitted file: function RMSE = rmse (data, k) %INSERT CODE end ctivate Go to Settings