Distance from the truth and residual, for both methods, with 100% of the noise in the matrix
At its defaults it draws distance from the truth and residual, for both methods, with 100% of the noise in the matrix. Four bars, medians over 40 seeds. The upper pair is how far each answer is from the coefficients the problem was built from; the lower pair is ‖Ax − b‖ on the problem as given. Ordinary least squares minimises the lower quantity by definition, so its bar is the shorter of the two whatever happens above — and at this share it is the less accurate answer.
residual-backwards is one function in lib/figures/tls.js —
errors in the matrix too — where the residual orders two methods backwards. Everything below came out of it during this build, at
arguments taken from the essays rather than invented for this page. A figure here is the
figure a reader meets in an essay, and if the generator changes, this page changes with it.
At its defaults
Drawn even though every essay passes arguments — which on this site is every essay, at 100% of placements since the standard pass. A default nothing exercises is a trap for the next essay to call this with none, and this is the page where a default that has drifted from the figures around it becomes visible.
Four bars, medians over 40 seeds. The upper pair is how far each answer is from the coefficients the problem was built from; the lower pair is ‖Ax − b‖ on the problem as given. Ordinary least squares minimises the lower quantity by definition, so its bar is the shorter of the two whatever happens above — and at this share it is the less accurate answer.
share: 0.85
The arguments are the ones The two numbers a caller has passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.
Four bars, medians over 40 seeds. The upper pair is how far each answer is from the coefficients the problem was built from; the lower pair is ‖Ax − b‖ on the problem as given. Ordinary least squares minimises the lower quantity by definition, so its bar is the shorter of the two whatever happens above — and at this share it is the less accurate answer.
share: 0.6
The arguments are the ones The two numbers a caller has passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.
Four bars, medians over 40 seeds. The upper pair is how far each answer is from the coefficients the problem was built from; the lower pair is ‖Ax − b‖ on the problem as given. Ordinary least squares minimises the lower quantity by definition, so its bar is the shorter of the two whatever happens above — and at this share it is the less accurate answer.
share: 0.6, total: 0.05
The arguments are the ones The two numbers a caller has passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.
Four bars, medians over 40 seeds. The upper pair is how far each answer is from the coefficients the problem was built from; the lower pair is ‖Ax − b‖ on the problem as given. Ordinary least squares minimises the lower quantity by definition, so its bar is the shorter of the two whatever happens above — and at this share it is the less accurate answer.
share: 0.6, total: 0.5
The arguments are the ones The two numbers a caller has passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.
Four bars, medians over 40 seeds. The upper pair is how far each answer is from the coefficients the problem was built from; the lower pair is ‖Ax − b‖ on the problem as given. Ordinary least squares minimises the lower quantity by definition, so its bar is the shorter of the two whatever happens above — and at this share it is the less accurate answer.
share: 0.4
The arguments are the ones The two numbers a caller has passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.
Four bars, medians over 40 seeds. The upper pair is how far each answer is from the coefficients the problem was built from; the lower pair is ‖Ax − b‖ on the problem as given. Ordinary least squares minimises the lower quantity by definition, so its bar is the shorter of the two whatever happens above — and at this share it is the less accurate answer.
What it checked while drawing
Every figure above checked its own claims on the way to being drawn, and a claim that failed
would have stopped the picture rather than shipped a wrong one. Those checks used to leave
no trace at all: a passing one returned true and the only evidence the figure had
checked anything was that nothing crashed. The list below is what they actually said, collected
by running this generator with an observer installed — not a description of
what it is believed to check.
6 distinct claims across 6 sets of arguments, grouped below by shape — because most of them are one sentence with a different number in it, and how many separate times that sentence was put to the test is the informative part.
a noise level the fit survives
a share of the noise between none and all
and at this share the more accurate answer is the one with the larger residual
and at this share the residual and the error agree, which is the case that hides the trap
enough seeds for a median to mean something
the total least-squares residual is the larger, whatever the share
Against the rule
It draws a decomposition and prints its residual. It calls
overSeeds,
and every figure above carries the badge — which residualcheck verifies by looking
for it in the emitted SVG rather than by finding the call that builds one. A badge that is
constructed and then left out of the body is the failure that check exists for.
Across the library: the rule bites on 217
of 397 generators —
199 print a residual and
18 are exempt with a published reason;
180 factorise nothing.
Read from lib/residual-rule.js, which is the same body the gate enforces from,
and the gate's last check fails the build if this page and it disagree about any generator.
Where it is called
Changing this generator changes every figure on this list. That is what makes the list worth publishing rather than keeping in a check script.
The two numbers a caller has
Choosing between the two least-squares methods is a statement about where the noise is, and the two quantities a caller can compute are both blind to it. The residual separates the answers by 0.14 per cent where their accuracies differ by 14, and κ(A) falls from 3.54 to 2.46 across a sweep in which the error rises by a factor of sixty-two.
Least squares, and the road not to takeWhen the matrix is wrong too
Every least-squares problem here has assumed A is exact and b is not, and moved b onto the column space of A. Where both were measured, the smallest correction that makes the system consistent moves the matrix as well — and on the problems where that answer is more accurate, it has the larger residual, by construction rather than by luck.