Residual values for a linear regression fit

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I have these points
x = [1,1,2,2,3,4,4,6]';
y = [8,1,1,2,2,3,4,1]';
I want to remove the point from above set that makes the residual largest.
This is the code I use
for i=1:length(x)
x_bk = x;
y_bk = y;
x(i) = [];
y(i) = [];
X = [ones(length(x),1) x];
b = X\y;
yhat = X*b;
d(i) = abs(sum(y - yhat));
x = x_bk;
y = y_bk;
index = find(min(d)==d);
x(index) = [];
y(index) = [];
X = [ones(length(x),1) x];
b = X\y;
yhat_r = X*b;
hold on
I think the result should be black line (attached file), but I get red dashed line.

Accepted Answer

Star Strider
Star Strider on 16 Oct 2020
I would do something like this:
x = [1,1,2,2,3,4,4,6]';
y = [8,1,1,2,2,3,4,1]';
xv = x;
yv = y;
for k = 1:numel(x)
X = [xv(:), ones(size(xv(:)))];
b = X \ yv(:);
yhat = X*b;
rsdn(k) = norm(yv - X*b);
xv = x;
yv = y;
xv(k) = [];
yv(k) = [];
plot((1:numel(x)), rsdn)
[rsdnmin,idxn] = min(rsdn(2:end));
[rsdnmax,idxx] = max(rsdn(2:end));
lowest = idxn+1
hihest = idxx+1
idxv = [lowest; hihest];
for k = 1:2
xv = x;
yv = y;
xv(idxv(k)) = [];
yv(idxv(k)) = [];
yhat = [xv(:), ones(size(xv(:)))]*bmtx(:,idxv(k));
hold on
plot(xv, yhat, '--r')
hold off
title(sprintf('Eliminating Set %d', idxv(k)))
Here, the norm of residuals (the usual metric) is least when eliminating ‘row=2’, and greatest when eliminating ‘row=6’.
Experiment to get the result you want.
NA on 17 Oct 2020
I want to show that if I remove only one set of data the regression line changes a lot. (But I do not know, this is practically true or not).
For this reason, I make this set:
a0 = 4.5882;
a1 = 0.2353;
x = (0:1:8)';
y = a0+a1*x+randn(size(x));
But, it does not show any difference (please see the attachment). I think the way of producing data set is not correct.
Star Strider
Star Strider on 17 Oct 2020
In that simulation, you are defining a particular slope and intercept and adding a normally-distributed random vector to it. The slopes and intercepts of the fitted lines will not change much.
You can see that most easily if you add this text call to each plot (in the loop):
text(1.1*min(xlim),0.9*max(ylim), sprintf('Y = %.3f\\cdotX%+.3f',bmtx(:,k)), 'HorizontalAlignment','left')
That will print the regression equation in the upper-left corner of each one. You can then compare them.
Note that the residual norms do not change much, either. In the original data set, they varied between 2.73 and 5.97. In this data set, they are within about ±0.5 of each other.

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