How do I correctly code with polyfit?

Hello everyone, I've been working on this problem for the last hour and I can't find a way to use polyfit and polyval like how they want it in the instructions

(1 pt) Enter the following data

x = 0 : 1 : 8;

y = [47, 12, 0, 40, 30, 36, 60, 110, 156];

(1 pt) Open a figure and plot the data with circle markers.

(3 pts) Use the polyfit() function to compute the coefficients of a 3rd-order fit to this data.

(3 pts) Set x3 = 0 : 0.2 : 8;

Use the polyval() function with the computed coefficients and the x3 vector to compute the y values of the 3rd-

order fit.

(2 pts) Use hold on and grid on.

Then plot the 3rd-order fit on the same figure.

My code

x = 0 : 1 : 8;

y = [47, 12, 0, 40, 30, 36, 60, 110, 156];

figure;

plot(x,y,'o')

[Jonas]

just call polyfit with x, y and your order. then, use the output argument with the polyval function and the new x values which are asked for

and yes, i could write down the code, but it's your homework

[Eduardo Gallegos]

Something like this?? Is this right??

x = 0 : 1 : 8;

y = [47, 12, 0, 40, 30, 36, 60, 110, 156];

figure;

plot(x,y,'o')

p = polyfit(x,y,3)

x3 = 0 : 0.2 : 8;

y3 = polyval(p,x3)

hold on;

grid on;

plot(x3,y3)

title('3rd order curve Fit to Data Points')

[Jonas]

perfect

but please use code format to post code in the forum here (see the most left button in the code tool section)

e.g.

x3 = 0:0.2:8;

instead of

x3 = 0:0.2:8;

Answer 1

[Image Analyst]

See my polyfit demo, attached.

Adapt as needed to replace my data with yours, like this:

% Demo to illustrate how to use the polyfit routine to fit data to a polynomial
% and to use polyval() to get estimated (fitted) data from the coefficients that polyfit() returns.
% Initialization steps.
clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
clear;  % Erase all existing variables. Or clearvars if you want.
workspace;  % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 20;
x = 0 : 1 : 8;
y = [47, 12, 0, 40, 30, 36, 60, 110, 156];

%============= CUBIC FIT ===================================
% Plot the training set of data.
plot(x, y, 'r.', 'MarkerSize', 40, 'LineWidth', 2);
grid on;
xlabel('X', 'FontSize', fontSize);
ylabel('Y', 'FontSize', fontSize);
title('Cubic Fit', 'FontSize', fontSize);
% Do the regression with polyfit to fit a cubic polynomial.
cubicCefficients = polyfit(x, y, 3)

% The x coefficient, slope, is coefficients(1).
% The constant, the intercept, is coefficients(2).
% Make fit.  It does NOT need to have the same
% number of elements as your training set, 
% or the same range, though it could if you want.
% Make 500 fitted samples going the whole range of x.
xFit = linspace(min(x), max(x), 500);
% Get the estimated values with polyval()
yFit = polyval(cubicCefficients, xFit);

% Plot the fit
hold on;
plot(xFit, yFit, 'b-', 'LineWidth', 2);
grid on;
legend('Training Set', 'Fit', 'Location', 'Northwest');
caption = sprintf('Cubic Fit.  Equation: y = %.2f * x^3 + %.2f * x^2 + %.2f * x + %.2f', ...
    cubicCefficients(1), cubicCefficients(2), cubicCefficients(3), cubicCefficients(4));
title(caption, 'FontSize', fontSize);