- Implement this in MATLAB algorithms for: Polynomial Least Squares Regression - For polynomial curve fitting, examine 1st to 5th polynomial order and determine the right order to be used using the least value of Akaike Information Criterion and Bayesian Information Criterion. - 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. 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; hold on; plot(avg_data(:, 1), avg_data(:, 2), 'k-', 'Linewidth', 1, 'DisplayName', 'Average Data Points'); 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;

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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- Please check for my errors. I am still learning.
- Implement this in MATLAB algorithms for: Polynomial Least Squares Regression
- For polynomial curve fitting, examine 1st to 5th polynomial order and determine the right order to be used using the least value of Akaike Information Criterion and Bayesian Information Criterion.
- 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.

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

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;

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- Implement this in MATLAB algorithms for: Polynomial Least Squares Regression

- For polynomial curve fitting, examine 1st to 5th polynomial order and determine the right order to be used using the least value of Akaike Information Criterion and Bayesian Information Criterion.

- 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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