Posted on 07/11/2012 11:22:45 AM PDT by Ernest_at_the_Beach
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I will look thru them and post a few.
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Murray Grainger says:
1 in 2.6 is close enough to 1 in 1.6 million for the average climate alarmist; whats your beef? Nothing that a little data adjustment wont fix.
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Willis, Can I please borrow your brain for a few days.. I could make a gazillion dollars with all that extra smarts and speed of thought. I cant even comprehend the amount of work and effort that it even took to come up with the line of analysis, let alone sit down with the data. But then, I still dont have your brain. But then, unfortunately, not many scientists do either.
Thank you, Sir.
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Steve R says:
This whole 1 in 1.6 million issue has been great entertainment. Its also been an eye opener, to see so many climate scientists struggling with a fairly basic statistical concept.
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Poisson distribution From Wikipedia, the free encyclopedia
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In probability theory and statistics, the Poisson distribution (pronounced [pwasɔ̃]) is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time and/or space if these events occur with a known average rate and independently of the time since the last event.[1] (The Poisson distribution can also be used for the number of events in other specified intervals such as distance, area or volume.)
Suppose someone typically gets 4 pieces of mail per day. That becomes the expectation, but there will be a certain spread: sometimes a little more, sometimes a little less, once in a while nothing at all.[2] Given only the average rate, for a certain period of observation (pieces of mail per day, phonecalls per hour, etc.), and assuming that the process, or mix of processes, that produce the event flow are essentially random, the Poisson distribution specifies how likely it is that the count will be 3, or 5, or 11, or any other number, during one period of observation. That is, it predicts the degree of spread around a known average rate of occurrence.[2]
The distribution's practical usefulness has been explained by the Poisson law of small numbers.[3]
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Dave Wendt says:
It would appear that inadvertently Mr. Masters, or whoever provided him with his numbers, has arrived at a ratio that is quite correct, the only problem being the ratio is applied to the wrong query. If you ask what are the odds of a story about a human caused plague of horrendous heatwaves, which appears in any Lamestream Media source, NOT being complete BS? the ratio of 1 in 1.6 million appears, to my eye at least, to be just about spot on.
fyi
Anyway just letting you know about it.
Related thread:
May I?... or do you want to?
I think a lot of eyes see stuff here.
OK Ernest. I’ll post it to the general news forums.
I can’t post it. It is a blog site. I tried. FR bounced me.
Just saves the headache.
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As always, astounding reasoning, Willis. I find your conclusion flawless. I find that it also supports something that I have long suspected few people are actually qualified to work with statistics or make statistical pronouncements. From what I recall, Jeff was only quoting some ass at NOAA, so perhaps it isnt his fault. However, you really should communicate your reasoning to him. I think that there is absolutely no question that you have demonstrated that it is a Poisson process with significant autocorrelation indeed, from the histogram (exactly as one would expect) and that as you say if anything it suggests that there have (probably) been other thirteen month stretches. It is also interesting to note that the distribution peaks at 5 months. That is, the most likely number of months in a year to be in the top 1/3 is between 1/3 and 1/2 of them!
Yet according the reasoning of the unknown statistician at NOAA, the odds of having any interval of 5 months in the top third are . They seem to think that every month is an independent trial or something.
Sigh.
rgb