close all; clear all; clc; current_script = mfilename('fullpath'); script_directory = fileparts(current_script); file_name0 = 'data_00.csv'; file_name1 = 'data_01.csv'; file_name2 = 'data_02.csv'; file_name3 = 'data_03.csv'; data0 = csvread([script_directory '\' file_name0]); data1 = csvread([script_directory '\' file_name1]); data2 = csvread([script_directory '\' file_name2]); data3 = csvread([script_directory '\' file_name3]); avg_data = (data1 + data2 + data3) / 3; figure; % plot (data0(:,1), data0(:,2), 'b-', 'LineWidth', 2, 'DisplayName', 'Parabolic Curve'); hold on; % plot(avg_data(:, 1), avg_data(:, 2), 'k-', 'Linewidth', 1, 'DisplayName', 'Average Data Points'); plot(smooth_data(:, 1), smooth_data(:, 2), 'k-', 'Linewidth', 1, 'DisplayName', 'Smoothed Data'); scatter(data1(:,1), data1(:,2), 5, 'r', 'filled', 'DisplayName', 'Sample Data Points 1'); scatter(data2(:,1), data2(:,2), 5, 'g', 'filled', 'DisplayName', 'Sample Data Points 2'); scatter(data3(:,1), data3(:,2), 5, 'y', 'filled', 'DisplayName', 'Sample Data Points 3'); scatter(avg_data(:,1), avg_data(:,2), 5, 'k', 'filled', 'DisplayName', 'Average Data Points'); title('Parabolic Function with Noisy Data Points'); xlabel('x'); ylabel('y'); legend('Location', 'North'); grid on; hold off; - Correct for my errors - Implement in MATLAB the algorithms for: Lagrange Interpolating Polynomial (LIP) - For polynomial curve fitting, examine 1st to 5th polynomial order then determine the right order to be used using the least value of AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion). Discuss the concepts and discuss the differences between AIC and BIC. - For the final evaluation of your curve fitting functions, use the Root Mean Square Error and Mean Absolute Error as the final metrics against Data 00. - Include plots/graphs.

COMPREHENSIVE MICROSOFT OFFICE 365 EXCE

1st Edition

ISBN:9780357392676

Author:FREUND, Steven

Publisher:FREUND, Steven

Chapter7: Creating Templates, Importing Data, And Working With Smartart, Images, And Screenshots

Section: Chapter Questions

Problem 4AYK

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Question

close all;
clear all;
clc;

current_script = mfilename('fullpath');
script_directory = fileparts(current_script);

file_name0 = 'data_00.csv';
file_name1 = 'data_01.csv';
file_name2 = 'data_02.csv';
file_name3 = 'data_03.csv';

data0 = csvread([script_directory '\' file_name0]);
data1 = csvread([script_directory '\' file_name1]);
data2 = csvread([script_directory '\' file_name2]);
data3 = csvread([script_directory '\' file_name3]);

avg_data = (data1 + data2 + data3) / 3;

figure;
% plot (data0(:,1), data0(:,2), 'b-', 'LineWidth', 2, 'DisplayName', 'Parabolic Curve');

hold on;
% plot(avg_data(:, 1), avg_data(:, 2), 'k-', 'Linewidth', 1, 'DisplayName', 'Average Data Points');
plot(smooth_data(:, 1), smooth_data(:, 2), 'k-', 'Linewidth', 1, 'DisplayName', 'Smoothed Data');

scatter(data1(:,1), data1(:,2), 5, 'r', 'filled', 'DisplayName', 'Sample Data Points 1');
scatter(data2(:,1), data2(:,2), 5, 'g', 'filled', 'DisplayName', 'Sample Data Points 2');
scatter(data3(:,1), data3(:,2), 5, 'y', 'filled', 'DisplayName', 'Sample Data Points 3');
scatter(avg_data(:,1), avg_data(:,2), 5, 'k', 'filled', 'DisplayName', 'Average Data Points');
title('Parabolic Function with Noisy Data Points'); xlabel('x'); ylabel('y'); legend('Location', 'North');
grid on;
hold off;

- Correct for my errors
- Implement in MATLAB the algorithms for: Lagrange Interpolating Polynomial (LIP)
- For polynomial curve fitting, examine 1st to 5th polynomial order then determine the right order to be used using the least value of AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion). Discuss the concepts and discuss the differences between AIC and BIC.
- For the final evaluation of your curve fitting functions, use the Root Mean Square Error and Mean Absolute Error as the final metrics against Data 00.
- Include plots/graphs.

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What if the data given gives a parabolic curve function? How to fix the linear line? Pls give me the correct code.

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Polynomial Curve Fitting and Model Selection
Sample Data Points 1
Sample Data Points 2
Sample Data Points 3
Average Data Points
Order 1 Polynomial Fit
Order 2 Polynomial Fit
Order 3 Polynomial Fit
Order 4 Polynomial Fit
- Order 5 Polynomial Fit
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X
5
10

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