Use a MATLAB function crossv.m that carries out cross-validation to determine the mean and standard deviation of the true data points from the predicted lines for a polynomial of a specified order, in y vs. x. (For input arguments, use the variables x, y and degree of polynomial.) Test the function on the age vs. depth data used in agedepth.txt. Provide the published code, and run it on the age vs. depth data for the polynomials of order 1 through 6. What conclusions about the goodness-of-fits do you draw (for increasing order of polynomial), and why --------------crossv.m-------------- function [avgerr, avgdev]= crossv(x,y, norder) % crossv function to perform crossvalidation of y vs. x (inputs) % inputs: x, y (both are single-variable vectors the same length), norder % norder is the order of the polynomial for fitting % outputs: average and standard deviation of predicted values of y from x % (avgerr, avgdev), as based on cross-validation % GMA, Oct. 2008, based on a MATLAB Recipes routine by Trauth n = length(x); for i = 1 : n % Define temporary variables j_x and j_y j_x = x; j_y = y; % Eliminate the i-th data point j_x(i) = []; j_y(i) = []; % Compute regression line from the n-1 data points p(i,:) = polyfit(j_x,j_y,norder); plot(x,polyval(p(i,:),x),'r'); hold on % Store the regression result and errors in predy and prederr predy(i) = polyval(p(i,:),x(i)); prederr(i) = predy(i) - y(i); end avgerr = mean(prederr); avgdev = std(prederr); 

------------------------------------------------agedepth.txt--------------------------------------------------------

 5.7496426e-01 -3.3873585e+00 1.1900810e+00 2.6074261e+00 1.7792352e+00 7.6639203e-03 2.2667996e+00 1.9777412e+01 3.2289751e+00 1.9460857e+01 4.0175282e+00 4.7864249e+01 5.4261635e+00 1.5663317e+01 5.4617632e+00 2.8686956e+01 6.5791060e+00 4.8130676e+01 7.1331107e+00 4.0977152e+01 8.1814463e+00 5.8764541e+01 8.6888108e+00 4.0716317e+01 9.4808290e+00 5.9144081e+01 9.5638885e+00 7.5251810e+01 9.9660919e+00 5.9726624e+01 1.0737034e+01 7.0804379e+01 1.1249168e+01 4.4681634e+01 1.1910960e+01 7.4737244e+01 1.1924943e+01 6.3437347e+01 1.1943416e+01 7.4103329e+01 1.2332423e+01 6.7087385e+01 1.2524693e+01 7.3214110e+01 1.3400077e+01 7.6690174e+01 1.5091028e+01 6.8961248e+01 1.5822464e+01 7.0582863e+01 1.6299041e+01 8.8108010e+01 1.6589485e+01 7.7996858e+01 1.7965035e+01 1.1009416e+02 1.8179787e+01 1.1946161e+02 1.9122448e+01 1.2067323e+02