How to calculate 2 signals errors from excel datasheets?

Hi,

I have 6 excel datasheets, each sheet contains 5 data signals. each data signals contains (measured (x,y) and calculated fits (x,y) data), which measured in different locations.

I need to calculate the errors (mean, standard deviation, root mean square) between the measured and calculated data. Then get the average of all errors of all 5 data signals

ex: datasheet 1 signal 1 mean error = 2 signal 2 mean error = 5 signal 3 mean error = 1 signal 4 mean error = 2.5 signal 5 mean error = 1.5

I want to get the average mean error of datasheet 1-6, separately.

Issue: measured data has a different range of calculated data and it has to be same data range to calculate the errors.

The code:

close all ; clear ; clc ; tic ;

filename = 'Cell_Error.xlsx'; % 5 Datasheets {HNC_2_07, HNC_2_09, HNC_2_10, U87_02, U87_03, U87_05} sheetnames = {'HNC_2_07'}; % Measured and Calculated Data xlRange = {'A3:E2135', 'G3:K2135', 'M3:Q2135', 'S3:W2135', 'Y3:AC2135' };% 'A3:B2135', 'G3:H2135', 'M3:N2135', 'S3:T2135', 'Y3:Z2135'};

n = length(sheetnames); m = length(xlRange);

figure(1); hold on data=zeros(1,1);

for idx = 1:n; idx; mn_tot =0; st_tot =0; Err_tot =0; SErr_tot =0; MSErr_tot = 0; RMSE_tot = 0;

    for idl = 1:m;
        idl;
        data = xlsread(filename,sheetnames{idx},xlRange{idl});

        Disp_Meas =data(:,1); % Measured Displacement 1nd column of the data
        Force_Meas =data(:,2); % Measured Foece 2ed column of the data

        Disp_Calc = data(:,4); % Calculated Displacement 4th column of the data
        Force_Calc = data(:,5); % Calculated Force 5th column of the data

        % NOTE: THERE ARE SOME NANS IN THE DATA. If you want to remove those, use
        % isnan():
        % Measured
        badRows = isnan(Force_Meas); % Logical vector.
        Disp_Meas = Disp_Meas(~badRows); % Extract good rows.
        Force_Meas(badRows) = []; % Alternate way to remove, set bad rows equal to null.
        % Calculated
        badRows = isnan(Force_Calc); % Logical vector.
        Disp_Calc = Disp_Calc(~badRows); % Extract good rows.
        Force_Calc(badRows) = []; % Alternate way to remove, set bad rows equal to null.

        % Interpolate Force_Calc y data so that it's estimated at the locations of the
        % Disp_Meas x locations:
        yCalcInterpolated = interp1(Disp_Calc, Force_Calc, Disp_Meas);
        %yMeasInterpolated = interp1(Disp_Meas, Force_Meas, Disp_Calc);

        % Least Squear Errors
        mn = mean((Disp_Calc-Disp_Meas).^2) % Mean Error
        st = std((Disp_Calc-Disp_Meas).^2) % Standred diviation Error
        Err = (Disp_Meas - mn)    % Errors
        SErr = (Disp_Meas - mn).^2   % Squared Error
        MSErr= mean((Err).^2)   % Mean Squared Error
        RMSE = sqrt(MSErr);  % Root Mean Squared Error

        % Add old error to the new error
        mn_tot = mn_tot+ mn;
        st_tot = st_tot+ st;
        Err_tot = Err_tot + Err;
        SErr_tot =SErr_tot + SErr;
        MSErr_tot = SErr_tot + SErr;
        RMSE_tot =  SErr_tot + SErr;

        % Plot them both at the Disp_Meas locations.
        plot(Disp_Meas, Force_Meas, 'k.-', 'LineWidth', 2, 'MarkerSize', 10);
        hold on;
        % Plot fitted Calcualtion.
        plot(Disp_Meas, yCalcInterpolated, 'r.-', 'LineWidth', 1, 'MarkerSize', 10);
        %plot(Disp_Meas,Force_Meas,Disp_Calc,Force_Calc);
        title('Force-Displacement Curve')
        xlabel('Displacement [nm]')
        ylabel('Force [nN]')
        legend('Measured Data','Calculated Data')

    end
    % Average Errors
    mn_average = mn_tot/m;
    st_average = st_tot/m;
    Err_average = Err_tot/m;
    SErr_average =SErr_tot/m;
    MSErr_average = SErr_tot/m;
    RMSE_average =  SErr_tot/m;
end

[Tariq Bahwini]

The curve of measure and calculated data look like this. Also, the picture of excel file is attached