There are basic principles of statistics and inference that the AGW proponents fail on. No matter what fancy methods you use they will not fix fundamental problems with the data captured. The whole AGW inference mechanism is fatally flawed and will serve in future as a perfect example of the misuse of statistics. For now post-modern science rules, propagated by those who wish to garner fame or fortune and fuelled by a parasitic industry that feeds on our fears.

This post may contain elementary principles but if one thinks behind the issues at what can really go wrong then they are probably more than enough to categorise what is going wrong in this fiasco of our times. Question: how was the 90% sure figure that the IPCC quote (as the likelihood of the A in AGW being right) determined? It’s rather simpler I suspect that using the Normal Distribution, application of the Central Limit Theorem, Sampling Theory, Hypothesis Testing or even flipping coins. It’s what most people call, in technical terms, an off the top of one’s head estimate.

Another question: When gathering information about something and forming opinion will one get a balanced view by review of the papers? You’re in effect summarising data, will the average of the paper views have a nice distribution (CLT) that we can infer from? Sure if you take enough info then the distribution will tend to be normal (woohoo!) but it’s hiding a fundamental problem with the data in terms of bias (unless the papers are unbiased). So you get a nice normal distribution for your mean but because the data were biased in the first place it’s located around the wrong mean and very likely has the wrong spread. In technical terms it’s rubbish.

]]>I also flubbed at least the link to fnoise.m. Here it is as well as a tarball of the project, which includes the text files for the spectra, histograms, and matlab pdfs for one particular run.

]]>Like SOD, I use MATLAB/Octave for most of my work. I don’t have the slightest idea how you’d implement this in R, though I’m sure it’s very doable.

First here’s a function that produces an interval of 1/f^nu noise.

Here’s the usage:

function noise = fnoise(npoints, nu)

Just give it how many points you want to generate, and the exponent for the noise, and it does the rest. One note, it is set to give zero DC offset. For S(f) = 1/f^nu noise, this means S(0) = 0.

Again here’s the usage:

function anoise = fnoiseavg(ntrials, navg, nu)

Note that this function has fnoise embedded in it (trying to make it simpler for people who don’t have great matlab familiarity).

I’ve also written a driver that demonstrates the effect of varying nu.

This should have all the functions needed to make it run.

Here’s what the waveforms looks like.

What you find (briefly) is that the only case where you get true gaussian noise in the limit of large n, is nu = 0. However, this is the case for “white noise”, which is uncorrelated. Values of nu 0 (“red noise”) give either fattened (platykurtic) or “double peaked” distributions.

I can explain why that happens, but it’s a bit technical.

The values of nu used were -2.0, -1.5, -1.0, -0.5, 0.0, 0.5, 1.0, 1.5, and 2.0. (The color selections are the same in each case.)

The matlab script I’ve linked to generates PDF plots, but they will look visually different than the case I’ve posted here.

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