To get it to plot in a new figure, you need to call the figure() function. Otherwise it just blasts over the old/existing figure. Corrected code:
% MAE 340 Lecture_18HW. Least-Squares Regression.
clear
clc
x = 5:5:50; % Declare x values from data set.
y = [17 24 31 33 37 37 40 40 42 41]; % Declare y values from data set.
% Run mylinregr_linear.m. Least-squares linear regression.
[a, r2] = mylinregr_linear(x,y); % <--- I get a yellow warning for this line, saying that a & r2 might not be used
% Run mylinregr_parabola.m. Least-squares linear regression.
[a, r2] = mylinregr_parabola(x,y);
% Here's my linear regression function:
function [a, r2] = mylinregr_linear(x,y)
% mylinregr_linear: linear regression curve fitting
% [a, r2] = linregr(x,y): Least squares fit of straight line to data by
% solving the normal equations.
% input:
% x = independent variable
% y = dependent variable
% output:
% a = vector of slope, a(2), and intercept, a(1)
% r2 = coefficient of determination
n = length(x);
if length(y)~=n, error('x and y must be same length'); end
x = x(:); y = y(:); % convert to column vectors
sx = sum(x); sy = sum(y);
sx2 = sum(x.*x); sxy= sum(x.*y); sy2 = sum(y.*y);
a(2) = (n*sxy-sx*sy)/(n*sx2-sx^2);
a(1) = sy/n-a(2)*sx/n;
r2 = ((n*sxy-sx*sy)/sqrt(n*sx2-sx^2)/sqrt(n*sy2-sy^2))^2;
% create plot of data and best fit line
xp = linspace(min(x),max(x),2);
yp = a(2)*xp+a(1);
figure
plot(x,y,'o',xp,yp)
grid on
end
% Here's my polynomial/parabolic regression function:
function [a, r2] = mylinregr_parabola(x,y)
% mylinregr_parabola: polynomial regression curve fitting
% [a, r2] = linregr(x,y): Least squares fit of a parabola to data by
% solving the normal equations
% input:
% x = independent variable
% y = dependent variable
% output:
% a = coefficients of the least-squares quadratic
% r2 = coefficient of determination
n = length(x);
if length(y)~=n, error('x and y must be same length'); end
x = x(:); y = y(:); % convert to column vectors
Z = [ones(size(x)) x x.^2];
a = Z\y;
St = sum((y-mean(y)).^2);
Sr = sum((y-Z*a).^2);
r2 = 1-Sr/St;
% create plot of data and best fit line
p = polyfit(x,y,2);
xp = linspace(min(x),max(x));
yp = polyval(p,xp);
figure
plot(x,y,'o',xp,yp)
grid on
end
