OT: Excess deaths stats

Sylvia Else

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Feb 14, 2024, 12:57:38 AMFeb 14

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<https://www.abs.gov.au/statistics/health/causes-death/provisional-mortality-statistics/latest-release>

I just need a sanity check before I raise this with the relevant agency.
If you scroll down to "Age specific rates, 2023, 2022, Baseline", and
look at the three right hand columns.

How can the 2023 figures for each age group be less than the
corresponding baseline average, but the all-ages number be greater than
the baseline average?

If any 2023 age group were above the baseline average, then all-ages
number could go either way, because of different total populations in
each age group, but with all age groups being below the baseline
average, I just don't see it.

This seems to happen not just for both sexes, but for each sex individually.

Sylvia.

Anthony William Sloman

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Feb 14, 2024, 2:57:10 AMFeb 14

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On Wednesday, February 14, 2024 at 4:57:38 PM UTC+11, Sylvia Else wrote:
> <https://www.abs.gov.au/statistics/health/causes-death/provisional-mortality-statistics/latest-release>
>
> I just need a sanity check before I raise this with the relevant agency.
> If you scroll down to "Age specific rates, 2023, 2022, Baseline", and
> look at the three right hand columns.
>
> How can the 2023 figures for each age group be less than the
> corresponding baseline average, but the all-ages number be greater than
> the baseline average?
>
> If any 2023 age group were above the baseline average, then all-ages
> number could go either way, because of different total populations in
> each age group, but with all age groups being below the baseline
> average, I just don't see it.

It's simple enough. The Covid-19 epidemic killed off quite a few vulnerable people, and the survivors represent samples from the more robust elements of the population.

Years ago it was claimed that males who survived past 80 in good health and women who survived past 85 in good health were predominantly drawn from that more robust population.

It's a statistician's play ground. In practical terms you can't work out precisely what population you belong to - you might just be a lucky snowflake.

An Australian male has an average life expectancy of 81.2 years, so at 81 and a couple of months I might be expected to drop dead soon. As an Australian male of 81, I've actually
got a life expectancy of 8.44 more years. In reality, as an 81-year-old Australian male who never smoked and managed to get a Ph.D. I'm a member of an even longer-lived cohort, but they don't split the life expectation table finely enough that I can quote a number.

> This seems to happen not just for both sexes, but for each sex individually.

Both sexes have been selected in much the same way.

--
Bill Sloman, Sydney

Sylvia Else

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Feb 14, 2024, 3:10:34 AMFeb 14

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I'm only concerned here about the relationship between the numbers,
which I cannot make any sense of.

Sylvia.

Anthony William Sloman

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Feb 14, 2024, 6:54:17 AMFeb 14

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> I'm only concerned here about the relationship between the numbers, which I cannot make any sense of.

Presumably the baseline averages go back before Covid-19 was killing people. It isn't killing as many people in 2023 as it was in 2022. but it is still killing some.

Covid-19 kills off more elderly people than young ones, so there are relatively fewer people in the elderly cohorts in 2023 than in 2022. Australia's population is growing so the more numerous younger people are preventing the average age from actually falling.

--
Bill Sloman, Sydney

legg

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Feb 14, 2024, 8:05:21 AMFeb 14

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On Wed, 14 Feb 2024 16:57:30 +1100, Sylvia Else <syl...@email.invalid>
wrote:

><https://www.abs.gov.au/statistics/health/causes-death/provisional-mortality-statistics/latest-release>
>
>I just need a sanity check before I raise this with the relevant agency.
>If you scroll down to "Age specific rates, 2023, 2022, Baseline", and
>look at the three right hand columns.
>
>How can the 2023 figures for each age group be less than the
>corresponding baseline average, but the all-ages number be greater than
>the baseline average?

Age specific rates, 2023, 2022, Baseline

I read all rates higher than baseline except in 85+.
. . . . including all-age.

Monthly rates also higher all months but one.

>
>If any 2023 age group were above the baseline average, then all-ages
>number could go either way, because of different total populations in
>each age group, but with all age groups being below the baseline
>average, I just don't see it.
>
>This seems to happen not just for both sexes, but for each sex individually.
>
>Sylvia.

?

RL

Martin Brown

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Feb 14, 2024, 9:41:41 AMFeb 14

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On 14/02/2024 05:57, Sylvia Else wrote:
> <https://www.abs.gov.au/statistics/health/causes-death/provisional-mortality-statistics/latest-release>
>
> I just need a sanity check before I raise this with the relevant agency.
> If you scroll down to "Age specific rates, 2023, 2022, Baseline", and
> look at the three right hand columns.

I see what you mean. It doesn't seem to make any sense.

All the numbers for Jan-Sept 2023 are below long term average (because a
proportion of those who would naturally have died in 2023 were killed
permaturely by Covid). The column total is above the long term average.

You are right - this data makes no sense at all.

> How can the 2023 figures for each age group be less than the
> corresponding baseline average, but the all-ages number be greater than
> the baseline average?

It can't even with really weird population ratings.

> If any 2023 age group were above the baseline average, then all-ages
> number could go either way, because of different total populations in
> each age group, but with all age groups being below the baseline
> average, I just don't see it.
>
> This seems to happen not just for both sexes, but for each sex
> individually.
>
> Sylvia.

I'll hazard a guess that some of the numbers presented there are summed
over a different period so that the larger numbers are not correct.

Spreadsheets allow people to make very creative mistakes!

--
Martin Brown

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darius

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a a

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Sylvia Else <syl...@email.invalid> wrote:

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darius

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darius

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darius

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Fred Bloggs

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Feb 14, 2024, 12:01:24 PMFeb 14

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The article is about EXCESS deaths. Care to explain how they would know what that is if they don't know unexcessive death rate is? The answer is they can't. They use historical rate data to estimate the unexcessive rates, called baseline. As they say:

"The purpose of a baseline is to provide a typical year (or combination of years) to compare the current year to. Deaths for 2023 will have two comparisons points - they will be compared to both deaths occurring in 2022 and a baseline period consisting of the average number of deaths occurring in the years of 2017-2019, 2021."

Apparently the variational statistics, that dictate the number of years used in baseline estimation, works out to 3 years at whatever confidence they're striving for.

Fred Bloggs

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Feb 14, 2024, 12:06:53 PMFeb 14

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On Wednesday, February 14, 2024 at 2:57:10 AM UTC-5, Anthony William Sloman wrote:
> On Wednesday, February 14, 2024 at 4:57:38 PM UTC+11, Sylvia Else wrote:
> > <https://www.abs.gov.au/statistics/health/causes-death/provisional-mortality-statistics/latest-release>
> >
> > I just need a sanity check before I raise this with the relevant agency.
> > If you scroll down to "Age specific rates, 2023, 2022, Baseline", and
> > look at the three right hand columns.
> >
> > How can the 2023 figures for each age group be less than the
> > corresponding baseline average, but the all-ages number be greater than
> > the baseline average?
> >
> > If any 2023 age group were above the baseline average, then all-ages
> > number could go either way, because of different total populations in
> > each age group, but with all age groups being below the baseline
> > average, I just don't see it.
> It's simple enough. The Covid-19 epidemic killed off quite a few vulnerable people, and the survivors represent samples from the more robust elements of the population.
>
> Years ago it was claimed that males who survived past 80 in good health and women who survived past 85 in good health were predominantly drawn from that more robust population.
>
> It's a statistician's play ground. In practical terms you can't work out precisely what population you belong to - you might just be a lucky snowflake.
>
> An Australian male has an average life expectancy of 81.2 years, so at 81 and a couple of months I might be expected to drop dead soon. As an Australian male of 81, I've actually

Not really. That life statistic has a standard deviation to it. I don't know what it is for males age 72-88, but for the general population, it is 8 years. That would be 8 years either side of the mean. 2/3 of people die with a standard deviation of the mean. So that would be 33% in the range mean + 8 years. That's not called dropping dead soon.

Fred Bloggs

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Feb 14, 2024, 12:14:53 PMFeb 14

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There's another extreme value statistic that shows death rate departs from the simplified exponential at the extremes, making for the conditional finding that the longer one has lived increases the chances of continuing to live. If you make it to 100, you're much less likely to die within the year than an 80 yo.

>
> --
> Bill Sloman, Sydney

Anthony William Sloman

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Feb 14, 2024, 10:32:40 PMFeb 14

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On Thursday, February 15, 2024 at 4:06:53 AM UTC+11, Fred Bloggs wrote:
> On Wednesday, February 14, 2024 at 2:57:10 AM UTC-5, Anthony William Sloman wrote:
> > On Wednesday, February 14, 2024 at 4:57:38 PM UTC+11, Sylvia Else wrote:
> > > <https://www.abs.gov.au/statistics/health/causes-death/provisional-mortality-statistics/latest-release>
> > >
> > > I just need a sanity check before I raise this with the relevant agency.
> > > If you scroll down to "Age specific rates, 2023, 2022, Baseline", and
> > > look at the three right hand columns.
> > >
> > > How can the 2023 figures for each age group be less than the
> > > corresponding baseline average, but the all-ages number be greater than
> > > the baseline average?
> > >
> > > If any 2023 age group were above the baseline average, then all-ages
> > > number could go either way, because of different total populations in
> > > each age group, but with all age groups being below the baseline
> > > average, I just don't see it.
> > It's simple enough. The Covid-19 epidemic killed off quite a few vulnerable people, and the survivors represent samples from the more robust elements of the population.
> >
> > Years ago it was claimed that males who survived past 80 in good health and women who survived past 85 in good health were predominantly drawn from that more robust population.
> >
> > It's a statistician's play ground. In practical terms you can't work out precisely what population you belong to - you might just be a lucky snowflake.
> >

> > An Australian male has an average life expectancy of 81.2 years, so at 81 and a couple of months I might be expected to drop dead soon. As an Australian male of 81, I've actually As an Australian male of 81, I've actually got a life expectancy of 8.44 more years.

In reality, as an 81-year-old Australian male who never smoked and managed to get a Ph.D. I'm a member of an even longer-lived cohort, but they don't split the life expectation table finely enough that I can quote a number.
>
> Not really. That life statistic has a standard deviation to it. I don't know what it is for males age 72-88, but for the general population, it is 8 years. That would be 8 years either side of the mean. 2/3 of people die with a standard deviation of the mean. So that would be 33% in the range mean + 8 years. That's not called dropping dead soon.

As usual, you managed to insert you comment in the middle of my paragraph,

The expectation of life at a given age does tend to shrink as you get older, and the standard deviation can be expected to shrink in proportion. Your assertion assumes that the subsequent life spans will be normally distributed, and they won't be.

I'm not much of a statistician, but you even worse informed

--
Bill Sloman, Sydney

Anthony William Sloman

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Feb 14, 2024, 10:48:28 PMFeb 14

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On Thursday, February 15, 2024 at 4:14:53 AM UTC+11, Fred Bloggs wrote:
> On Wednesday, February 14, 2024 at 6:54:17 AM UTC-5, Anthony William Sloman wrote:
> > On Wednesday, February 14, 2024 at 7:10:34 PM UTC+11, Sylvia Else wrote:
> > > On 14-Feb-24 6:57 pm, Anthony William Sloman wrote:
> > > > On Wednesday, February 14, 2024 at 4:57:38 PM UTC+11, Sylvia Else wrote:

<snip>

> > > > Years ago it was claimed that males who survived past 80 in good health and women who survived past 85 in good health were predominantly drawn from that more robust population.

<snip>

> There's another extreme value statistic that shows death rate departs from the simplified exponential at the extremes, making for the conditional finding that the longer one has lived increases the chances of continuing to live. If you make it to 100, you're much less likely to die within the year than an 80 yo.

Not true. There s a dog leg in the statistics at around 80 for male and 85 for females, but at best if means that you are marginally less likely to die

https://www.ssa.gov/oact/STATS/table4c6.html

gives the actual numbers.

For males the probability of dying in the next year at age 80 is 0.065568, and at age 100 its 0.384967, so it is six times more likely, not less.

--
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Sylvia Else

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Feb 15, 2024, 12:22:35 AMFeb 15

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Thanks.

There's a update due in a couple of weeks. I'll wait to see whether that
suffers the same issue, and contact them if so. Otherwise I'll just
assume it was a one-off error.

Sylvia.

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Anthony William Sloman <bill....@ieee.org> wrote:

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> Subject: Re: OT: Excess deaths stats - sanity check
> From: Anthony William Sloman <bill....@ieee.org>

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darius

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Feb 15, 2024, 8:40:43 AMFeb 15

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The idiot Sylvia Else <syl...@email.invalid> persisting in being an Off-topic troll...

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Sylvia Else <syl...@email.invalid> wrote:

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> Subject: Re: OT: Excess deaths stats - sanity check

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Fred Bloggs

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Feb 15, 2024, 10:20:12 AMFeb 15

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All that is wrong, SSA is behind the times. My statement comes from recent medical based research.

>
> --
> Bill Sloman, Sydney

Anthony William Sloman

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Feb 15, 2024, 9:04:13 PMFeb 15

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It's not wrong. You are.You seem to have misunderstood whatever you think "your recent medical research" is telling you, not for the first time.

The data I quoted comes directly from the data on American people who actually died.

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Cursitor Doom

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Feb 16, 2024, 12:40:54 PMFeb 16

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On Thu, 15 Feb 2024 16:22:27 +1100, Sylvia Else <syl...@email.invalid>
wrote:

Worried about the Covid jabs you had then, Sylvia?

Fred Bloggs

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Feb 16, 2024, 1:44:50 PMFeb 16

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Your idea of being informed is a self-delusion.

The density function for years of life should be normal-like, a crude fit is said to be the log-normal, the logarithm of an underlying normal variate. Literature is calling it a survival distribution, which makes sense. If F(A) is the cumulative distribution (integrated ) of that density up to year A, indicating the fraction of population still alive by year A. Then the chance of an individual of age A living to age A + T, T being time interval of continued life, should be F( A + T)- F( A ). What you're after, whether you realize it or not is the distribution of T. Literature says it's an exponential distribution, and that makes no sense at all since it implies a constant death rate. If you can't compute the mean and standard deviation of that simple thing, then you have problems.

Writeup by some blithering person:

https://users.stat.ufl.edu/~rrandles/sta4930/4930lectures/chapter2/chapter2R.pdf

They think they're geniuses for fitting a Weibull.

Survival in general wiki:

https://en.wikipedia.org/wiki/Survival_function

>
> --
> Bill Sloman, Sydney

Fred Bloggs

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Feb 16, 2024, 1:47:45 PMFeb 16

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You're always saying that but the only person around here with amply demonstrated impaired comprehension is you.

>
> The data I quoted comes directly from the data on American people who actually died.

Phony data and analysis designed to make the place look like less of a hell-hole.
>
> --
> Bill Sloman, Sydney

darius

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Feb 16, 2024, 4:11:51 PMFeb 16

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Anthony William Sloman <bill....@ieee.org> wrote:

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> Subject: Re: OT: Excess deaths stats - sanity check

> From: Anthony William Sloman <bill....@ieee.org>

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a a

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Feb 16, 2024, 4:12:00 PMFeb 16

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The idiot Cursitor Doom <c...@notformail.com> persisting in being an Off-topic troll...

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Cursitor Doom <c...@notformail.com> wrote:

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> Subject: Re: OT: Excess deaths stats - sanity check

> Date: Fri, 16 Feb 2024 17:40:47 +0000

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Anthony William Sloman

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Feb 16, 2024, 10:51:59 PMFeb 16

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On Saturday, February 17, 2024 at 5:44:50 AM UTC+11, Fred Bloggs wrote:
> On Wednesday, February 14, 2024 at 10:32:40 PM UTC-5, Anthony William Sloman wrote:
> > On Thursday, February 15, 2024 at 4:06:53 AM UTC+11, Fred Bloggs wrote:
> > > On Wednesday, February 14, 2024 at 2:57:10 AM UTC-5, Anthony William Sloman wrote:
> > > > On Wednesday, February 14, 2024 at 4:57:38 PM UTC+11, Sylvia Else wrote:

<snip>

> > > > Years ago it was claimed that males who survived past 80 in good health and women who survived past 85 in good health were predominantly drawn from that more robust population.
> > > >
> > > > It's a statistician's play ground. In practical terms you can't work out precisely what population you belong to - you might just be a lucky snowflake.
> > > >
> > > > An Australian male has an average life expectancy of 81.2 years, so at 81 and a couple of months I might be expected to drop dead soon. As an Australian male of 81, I've actually As an Australian male of 81, I've actually got a life expectancy of 8.44 more years.
> > In reality, as an 81-year-old Australian male who never smoked and managed to get a Ph.D. I'm a member of an even longer-lived cohort, but they don't split the life expectation table finely enough that I can quote a number.
> > >
> > > Not really. That life statistic has a standard deviation to it. I don't know what it is for males age 72-88, but for the general population, it is 8 years. That would be 8 years either side of the mean. 2/3 of people die with a standard deviation of the mean. So that would be 33% in the range mean + 8 years. That's not called dropping dead soon.
> >
> > As usual, you managed to insert you comment in the middle of my paragraph,
> >
> > The expectation of life at a given age does tend to shrink as you get older, and the standard deviation can be expected to shrink in proportion. Your assertion assumes that the subsequent life spans will be normally distributed, and they won't be.
> >

> > I'm not much of a statistician, but you even worse informed.

>
> Your idea of being informed is a self-delusion.

Perhaps, but I'm clearly better-informed than you are.

> The density function for years of life should be normal-like, a crude fit is said to be the log-normal, the logarithm of an underlying normal variate. Literature is calling it a survival distribution, which makes sense. If F(A) is the cumulative distribution (integrated ) of that density up to year A, indicating the fraction of population still alive by year A. Then the chance of an individual of age A living to age A + T, T being time interval of continued life, should be F( A + T)- F( A ). What you're after, whether you realize it or not is the distribution of T. Literature says it's an exponential distribution, and that makes no sense at all since it implies a constant death rate. If you can't compute the mean and standard deviation of that simple thing, then you have problems.

A rather long-winded way of announcing that you don't know what you are talking about.

> Writeup by some blithering person:
>
> https://users.stat.ufl.edu/~rrandles/sta4930/4930lectures/chapter2/chapter2R.pdf
>
> They think they're geniuses for fitting a Weibull.

It's a shopping list of fitting functions. There's nothing in that write-up that shows the fit of an actual function to actual acturial data.

> Survival in general wiki:
>
> https://en.wikipedia.org/wiki/Survival_function

That is marginally better, in that it makes passing reference to real world breast cancer data, but it doesn't make any direct connection.

You do go to a lot of trouble to tell us that you don't know what you are talking about. The improved survival past age 80 for males and 85 for females might be susceptible to being modelled by a Weibull function - I wouldn't know. I could ask my cousin the statistician, but even though he is retired, I'd hate to waste his time on such a pointless question.

--
Bill Sloman, Sydney

Anthony William Sloman

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Feb 16, 2024, 10:57:34 PMFeb 16

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What "demonstrates" it to you is that I don't agree with the nonsense you post. What you post as what you imagine to be a "demonstration" merely reminds us that you have lost it.

> > The data I quoted comes directly from the data on American people who actually died.
>
> Phony data and analysis designed to make the place look like less of a hell-hole.

Cursitor Doom has the same attitude to climate change data. If it doesn't fit his preferred story, it has been faked.

--
Bill Sloman, Sydney

a a

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Feb 17, 2024, 1:14:41 AMFeb 17

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Anthony William Sloman <bill....@ieee.org> wrote:

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> Subject: Re: OT: Excess deaths stats - sanity check

> From: Anthony William Sloman <bill....@ieee.org>

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legg

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Feb 17, 2024, 9:21:06 AMFeb 17

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On Wed, 14 Feb 2024 08:06:12 -0500, legg <le...@nospam.magma.ca> wrote:

>On Wed, 14 Feb 2024 16:57:30 +1100, Sylvia Else <syl...@email.invalid>

>wrote:
>
>><https://www.abs.gov.au/statistics/health/causes-death/provisional-mortality-statistics/latest-release>
>>
>>I just need a sanity check before I raise this with the relevant agency.
>>If you scroll down to "Age specific rates, 2023, 2022, Baseline", and
>>look at the three right hand columns.
>>
>>How can the 2023 figures for each age group be less than the
>>corresponding baseline average, but the all-ages number be greater than
>>the baseline average?
>
>Age specific rates, 2023, 2022, Baseline
>

>I read all rates higher than baseline except in 85+.
>. . . . including all-age.
>
>Monthly rates also higher all months but one.

Sorry - was reading 'all deaths' chart, not age grouped table.

>>
>>If any 2023 age group were above the baseline average, then all-ages
>>number could go either way, because of different total populations in
>>each age group, but with all age groups being below the baseline
>>average, I just don't see it.
>>

>>This seems to happen not just for both sexes, but for each sex individually.
>>
>>Sylvia.

It looks like the anomaly in the report is resticted to 'persons'
and 'males'. It is not repeated in 'female' figures.

Neither parts of the table reflect trends in the charts.

RL

a a

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Feb 17, 2024, 9:34:42 AMFeb 17

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The arsehole legg <le...@nospam.magma.ca> persisting in being an Off-topic troll...

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legg <le...@nospam.magma.ca> wrote:

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> From: legg <le...@nospam.magma.ca>
> Newsgroups: sci.electronics.design

> Subject: Re: OT: Excess deaths stats - sanity check

> Date: Sat, 17 Feb 2024 09:22:00 -0500

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darius

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Feb 17, 2024, 9:34:43 AMFeb 17

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> Subject: Re: OT: Excess deaths stats - sanity check

> From: Anthony William Sloman <bill....@ieee.org>

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Anthony William Sloman

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Feb 17, 2024, 11:01:02 AMFeb 17

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On Sunday, February 18, 2024 at 1:21:06 AM UTC+11, legg wrote:
> On Wed, 14 Feb 2024 08:06:12 -0500, legg <le...@nospam.magma.ca> wrote:
> >On Wed, 14 Feb 2024 16:57:30 +1100, Sylvia Else <syl...@email.invalid>wrote:

<snip>

> It looks like the anomaly in the report is resticted to 'persons'
> and 'males'. It is not repeated in 'female' figures.
>
> Neither parts of the table reflect trends in the charts.

I don't think that any of these statistics are worth worrying about. Every human being is unique - identical twins do come close to being much the same, but while their basic genome is identical, even they develop slightly differently.

People age, and they don't age at exactly the same rate. Australia has lost 928 per million to Covid-19 - fewer than the US and the UK which at are about 3400 per million - which means that the survivors are a bit more robust than the population before the epidemic, but there's not a lot to be learned from small changes in the death rate post-epidemic, and you'd have to know what people were dying of to learn any thing useful.

Cursitor Doom does seem to think they might be dying from side-effects of the anti-Covid-19 vaccines, but that's because he's fond of lunatic conspiracy theories.

The one thing that did show up was that people stopped dying of flu during the pandemic - the anti-infection precautions against Covid-19 were even more effective at stopping flu spreading. - but we seem to be back to normal again now.

--
Bill Sloman. Sydney

Fred Bloggs

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Feb 17, 2024, 11:53:24 AMFeb 17

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On Friday, February 16, 2024 at 10:51:59 PM UTC-5, Anthony William Sloman wrote:
> On Saturday, February 17, 2024 at 5:44:50 AM UTC+11, Fred Bloggs wrote:
> > On Wednesday, February 14, 2024 at 10:32:40 PM UTC-5, Anthony William Sloman wrote:
> > > On Thursday, February 15, 2024 at 4:06:53 AM UTC+11, Fred Bloggs wrote:
> > > > On Wednesday, February 14, 2024 at 2:57:10 AM UTC-5, Anthony William Sloman wrote:
> > > > > On Wednesday, February 14, 2024 at 4:57:38 PM UTC+11, Sylvia Else wrote:
> <snip>
> > > > > Years ago it was claimed that males who survived past 80 in good health and women who survived past 85 in good health were predominantly drawn from that more robust population.
> > > > >
> > > > > It's a statistician's play ground. In practical terms you can't work out precisely what population you belong to - you might just be a lucky snowflake.
> > > > >
> > > > > An Australian male has an average life expectancy of 81.2 years, so at 81 and a couple of months I might be expected to drop dead soon. As an Australian male of 81, I've actually As an Australian male of 81, I've actually got a life expectancy of 8.44 more years.
> > > In reality, as an 81-year-old Australian male who never smoked and managed to get a Ph.D. I'm a member of an even longer-lived cohort, but they don't split the life expectation table finely enough that I can quote a number.
> > > >
> > > > Not really. That life statistic has a standard deviation to it. I don't know what it is for males age 72-88, but for the general population, it is 8 years. That would be 8 years either side of the mean. 2/3 of people die with a standard deviation of the mean. So that would be 33% in the range mean + 8 years. That's not called dropping dead soon.
> > >
> > > As usual, you managed to insert you comment in the middle of my paragraph,
> > >
> > > The expectation of life at a given age does tend to shrink as you get older, and the standard deviation can be expected to shrink in proportion. Your assertion assumes that the subsequent life spans will be normally distributed, and they won't be.
> > >
> > > I'm not much of a statistician, but you even worse informed.
> >
> > Your idea of being informed is a self-delusion.
> Perhaps, but I'm clearly better-informed than you are.

Of course you're going to think that, it's an ego preservation refuge for the megalomaniac.

> > The density function for years of life should be normal-like, a crude fit is said to be the log-normal, the logarithm of an underlying normal variate. Literature is calling it a survival distribution, which makes sense. If F(A) is the cumulative distribution (integrated ) of that density up to year A, indicating the fraction of population still alive by year A. Then the chance of an individual of age A living to age A + T, T being time interval of continued life, should be F( A + T)- F( A ). What you're after, whether you realize it or not is the distribution of T. Literature says it's an exponential distribution, and that makes no sense at all since it implies a constant death rate. If you can't compute the mean and standard deviation of that simple thing, then you have problems.
> A rather long-winded way of announcing that you don't know what you are talking about.

I know exactly what I'm talking about. The fact of you saying it's long winded goes to show how weak is your so-called analytical thinking.

> > Writeup by some blithering person:
> >
> > https://users.stat.ufl.edu/~rrandles/sta4930/4930lectures/chapter2/chapter2R.pdf
> >
> > They think they're geniuses for fitting a Weibull.
> It's a shopping list of fitting functions. There's nothing in that write-up that shows the fit of an actual function to actual acturial data.

It's a parameterized distribution used for fitting exponentials, and used extensively in modeling systems for reliability engineering lifetime statistics, just something else you don't know the first thing about.

> > Survival in general wiki:
> >
> > https://en.wikipedia.org/wiki/Survival_function
> That is marginally better, in that it makes passing reference to real world breast cancer data, but it doesn't make any direct connection.

Statistics is a tool of scientific discovery and not the science itself. Dunno what kind of childish arrested development would think it would be.

>
> You do go to a lot of trouble to tell us that you don't know what you are talking about.

You're too ignorant with an exacerbation of stupidity to make that assessment.

> The improved survival past age 80 for males and 85 for females might be susceptible to being modelled by a Weibull function - I wouldn't know. I could ask my cousin the statistician, but even though he is retired, I'd hate to waste his time on such a pointless question.

It's more than just a model. It does show that beyond a critical age range the death rate becomes constant, being directly proportional to the interval of time under consideration regardless of when that interval occurs, up to a limiting age when it rapidly breaks down.
You're too much of lightweight to understand any of that, so go ahead and call bullshit- the refrain of ignoramuses.

:

The exponential distribution is often concerned with the amount of time until some specific event occurs. For example, the amount of time (beginning now) until an earthquake occurs has an exponential distribution. Other examples include the length of time, in minutes, of long distance business telephone calls, and the amount of time, in months, a car battery lasts. It can be shown, too, that the value of the change that you have in your pocket or purse approximately follows an exponential distribution.

Values for an exponential random variable occur in the following way. There are fewer large values and more small values. For example, marketing studies have shown that the amount of money customers spend in one trip to the supermarket follows an exponential distribution. There are more people who spend small amounts of money and fewer people who spend large amounts of money.

Exponential distributions are commonly used in calculations of product reliability, or the length of time a product lasts.

The random variable for the exponential distribution is continuous and often measures a passage of time, although it can be used in other applications. Typical questions may be, “what is the probability that some event will occur within the next x
hours or days, or what is the probability that some event will occur between x1
hours and x2
hours, or what is the probability that the event will take more than x1
hours to perform?” In short, the random variable X equals (a) the time between events or (b) the passage of time to complete an action, e.g. wait on a customer. The probability density function is given by:

f(x)=1μe−1μx

where μ is the historical average waiting time.

and has a mean and standard deviation of 1/μ.

An alternative form of the exponential distribution formula recognizes what is often called the decay factor. The decay factor simply measures how rapidly the probability of an event declines as the random variable X increases.

When the notation using the decay parameter m is used, the probability density function is presented as:

f(x) = me−mx

where m=1μ

In order to calculate probabilities for specific probability density functions, the cumulative density function is used. The cumulative density function (cdf) is simply the integral of the pdf and is:

F(x)=∫∞0[1μe−xμ]=1−e−xμ '

:

Total waste of time to post that, you and numbers don't get along.

https://openstax.org/books/introductory-business-statistics/pages/5-3-the-exponential-distribution

I'm finding the business pages have the best explanations for statistical principles. They do the best job of making real sense of it. The 'nurd' pages are mostly jackass-inine factoid regurgitators. The nurds are used to being confused.

>
> --
> Bill Sloman, Sydney

Fred Bloggs

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Feb 17, 2024, 12:07:54 PMFeb 17

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On Saturday, February 17, 2024 at 11:01:02 AM UTC-5, Anthony William Sloman wrote:
> On Sunday, February 18, 2024 at 1:21:06 AM UTC+11, legg wrote:
> > On Wed, 14 Feb 2024 08:06:12 -0500, legg <le...@nospam.magma.ca> wrote:
> > >On Wed, 14 Feb 2024 16:57:30 +1100, Sylvia Else <syl...@email.invalid>wrote:
> <snip>
> > It looks like the anomaly in the report is resticted to 'persons'
> > and 'males'. It is not repeated in 'female' figures.
> >
> > Neither parts of the table reflect trends in the charts.
> I don't think that any of these statistics are worth worrying about. Every human being is unique - identical twins do come close to being much the same, but while their basic genome is identical, even they develop slightly differently.
>
> People age, and they don't age at exactly the same rate.

People die because of a lifetime of accumulated damage. If there was no accumulation of damages, everyone would live to 120. Individuality in death rate arises form individuality in exposure to damage. Your mortality curve is a survival function of damage.

Fred Bloggs

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Feb 17, 2024, 12:11:24 PMFeb 17

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She may be suffering from vaccine-induced dementia it looks like.

RichD

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Feb 17, 2024, 2:49:39 PMFeb 17

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On February 13, Sylvia Else wrote:
> <https://www.abs.gov.au/statistics/health/causes-death/provisional-mortality-statistics/latest-release>
>
> I just need a sanity check before I raise this with the relevant agency.
> If you scroll down to "Age specific rates, 2023, 2022, Baseline", and
> look at the three right hand columns.

> How can the 2023 figures for each age group be less than the
> corresponding baseline average, but the all-ages number be greater than
> the baseline average?

https://onlinelibrary.wiley.com/doi/10.1111/j.1745-3992.1988.tb00424.x
https://www.brainyquote.com/quotes/garrison_keillor_137097

Given Joltin' Joe and Sam Slugger, both on the same team.

In 2025, it's reported that Joe's batting average exceeds Sam's during
the first half of the season, also during the second half. Yet Sam's
average, over the entire season, exceeds Joe's.

Would you take this seriously?

--
Rich

RichD

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Feb 17, 2024, 3:00:56 PMFeb 17

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On February 17, Fred Bloggs wrote:
>> People age, and they don't age at exactly the same rate.
>
> People die because of a lifetime of accumulated damage. If there was no accumulation of
> damages, everyone would live to 120.

A firm manufactures a device, which includes a clock. The clock is guaranteed to
trigger an alarm after 35 years, with uncertainty ~5 years.

That is, the device is designed to operate at least that long, unless destroyed by
external forces. If it does survive that long, the clock and alarm is guaranteed.

Would you trust them? Do you know of any device with such a timer spec, manufactured
in very large quantities?

--
Rich

darius

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Feb 17, 2024, 4:06:41 PMFeb 17

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Sylvia Else

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Feb 17, 2024, 6:56:02 PMFeb 17

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My sole interest in this thread relates to having the correct data to
allow conclusions to be drawn. The data in question seem to be
internally inconsistent, which is to say, cannot be correct regardless
of the underlying facts.

Sylvia.

Anthony William Sloman

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Feb 17, 2024, 10:46:47 PMFeb 17

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On Sunday, February 18, 2024 at 3:53:24 AM UTC+11, Fred Bloggs wrote:
> On Friday, February 16, 2024 at 10:51:59 PM UTC-5, Anthony William Sloman wrote:
> > On Saturday, February 17, 2024 at 5:44:50 AM UTC+11, Fred Bloggs wrote:
> > > On Wednesday, February 14, 2024 at 10:32:40 PM UTC-5, Anthony William Sloman wrote:
> > > > On Thursday, February 15, 2024 at 4:06:53 AM UTC+11, Fred Bloggs wrote:
> > > > > On Wednesday, February 14, 2024 at 2:57:10 AM UTC-5, Anthony William Sloman wrote:
> > > > > > On Wednesday, February 14, 2024 at 4:57:38 PM UTC+11, Sylvia Else wrote:
> > <snip>
> > > > > > Years ago it was claimed that males who survived past 80 in good health and women who survived past 85 in good health were predominantly drawn from that more robust population.
> > > > > >
> > > > > > It's a statistician's play ground. In practical terms you can't work out precisely what population you belong to - you might just be a lucky snowflake.
> > > > > >
> > > > > > An Australian male has an average life expectancy of 81.2 years, so at 81 and a couple of months I might be expected to drop dead soon. As an Australian male of 81, I've actually As an Australian male of 81, I've actually got a life expectancy of 8.44 more years.
> > > > In reality, as an 81-year-old Australian male who never smoked and managed to get a Ph.D. I'm a member of an even longer-lived cohort, but they don't split the life expectation table finely enough that I can quote a number.
> > > > >
> > > > > Not really. That life statistic has a standard deviation to it. I don't know what it is for males age 72-88, but for the general population, it is 8 years. That would be 8 years either side of the mean. 2/3 of people die with a standard deviation of the mean. So that would be 33% in the range mean + 8 years. That's not called dropping dead soon.
> > > >
> > > > As usual, you managed to insert you comment in the middle of my paragraph,
> > > >
> > > > The expectation of life at a given age does tend to shrink as you get older, and the standard deviation can be expected to shrink in proportion. Your assertion assumes that the subsequent life spans will be normally distributed, and they won't be.
> > > >
> > > > I'm not much of a statistician, but you even worse informed.
> > >
> > > Your idea of being informed is a self-delusion.
> >
> > Perhaps, but I'm clearly better-informed than you are.
>
> Of course you're going to think that, it's an ego preservation refuge for the megalomaniac.

Funny that you, of all people, would suggest that. Look in the mirror.

> > > The density function for years of life should be normal-like, a crude fit is said to be the log-normal, the logarithm of an underlying normal variate. Literature is calling it a survival distribution, which makes sense. If F(A) is the cumulative distribution (integrated ) of that density up to year A, indicating the fraction of population still alive by year A. Then the chance of an individual of age A living to age A + T, T being time interval of continued life, should be F( A + T)- F( A ). What you're after, whether you realize it or not is the distribution of T. Literature says it's an exponential distribution, and that makes no sense at all since it implies a constant death rate. If you can't compute the mean and standard deviation of that simple thing, then you have problems.
> > A rather long-winded way of announcing that you don't know what you are talking about.
> I know exactly what I'm talking about. The fact of you saying it's long winded goes to show how weak is your so-called analytical thinking.
> > > Writeup by some blithering person:
> > >
> > > https://users.stat.ufl.edu/~rrandles/sta4930/4930lectures/chapter2/chapter2R.pdf
> > >
> > > They think they're geniuses for fitting a Weibull.
> >
> > It's a shopping list of fitting functions. There's nothing in that write-up that shows the fit of an actual function to actual acturial data.
>
> It's a parameterized distribution used for fitting exponentials, and used extensively in modeling systems for reliability engineering lifetime statistics, just something else you don't know the first thing about.

That's what I said. I don't know much about it because I've never had to do that - if my bosses wanted a more reliable system, we designed one that was more reliably by design, rather than by trrying to demonstrate that what we had was reliable enough.

> > > Survival in general wiki:
> > >
> > > https://en.wikipedia.org/wiki/Survival_function.

> >
> > That is marginally better, in that it makes passing reference to real world breast cancer data, but it doesn't make any direct connection.
>
> Statistics is a tool of scientific discovery and not the science itself. Dunno what kind of childish arrested development would think it would be.

You do seem to think exactly that.

> > You do go to a lot of trouble to tell us that you don't know what you are talking about.
>
> You're too ignorant with an exacerbation of stupidity to make that assessment.

Or so you like to think.

> > The improved survival past age 80 for males and 85 for females might be susceptible to being modelled by a Weibull function - I wouldn't know. I could ask my cousin the statistician, but even though he is retired, I'd hate to waste his time on such a pointless question.
>
> It's more than just a model. It does show that beyond a critical age range the death rate becomes constant, being directly proportional to the interval of time under consideration regardless of when that interval occurs, up to a limiting age when it rapidly breaks down.

Except that there isn't any kind of "critical age when the death rate becomes constant". The death rate depends on the environment, and it became higher when the Covid-19 virus became epidemic and went down again when most people had been vaccinated. People become more susceptible to all sorts of fatal conditions as they get older - in the US this includes not being fast enough on your feet to get away from people who have gone postal.

> You're too much of lightweight to understand any of that, so go ahead and call bullshit- the refrain of ignoramuses.

Or in this case, the refrain of somebody who does recognise bull-shit when he sees it.

<snipped total waste of bandwidth on cut-and paste>

> Total waste of time to post that, you and numbers don't get along.

We don't cooperate. I use them - which doesn't demand anything of them except their passive existence.

> I'm finding the business pages have the best explanations for statistical principles. They do the best job of making real sense of it. The 'nurd' pages are mostly jackass-inine factoid regurgitators. The nurds are used to being confused.

They keep it simple for the intellectually unambitious. At Melbourne University the brighter students studied economics and the dumber ones did business studies.
That means leaving out most of the interesting stuff.

--
Bill Sloman, Sydney

Anthony William Sloman

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Feb 17, 2024, 10:54:33 PMFeb 17

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On Sunday, February 18, 2024 at 10:56:02 AM UTC+11, Sylvia Else wrote:
> On 17-Feb-24 4:40 am, Cursitor Doom wrote:
> > On Thu, 15 Feb 2024 16:22:27 +1100, Sylvia Else <syl...@email.invalid> wrote:
> >> On 15-Feb-24 1:41 am, Martin Brown wrote:
> >>> On 14/02/2024 05:57, Sylvia Else wrote:

<snip>

> My sole interest in this thread relates to having the correct data to
> allow conclusions to be drawn. The data in question seem to be
> internally inconsistent, which is to say, cannot be correct regardless
> of the underlying facts.

I don't think we know enough about the data presented to have any idea whether it is internally consistent or not. It's almost certainly prepared by running real death counts through an automated process which we don't know much about. Because we don't know much about it we have to improvise our own ideas about what is going on, and it's easy to get that wrong.

--
Bill Sloman, Sydney

Fred Bloggs

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Feb 18, 2024, 8:58:35 AMFeb 18

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Nope- sounds kinda screwy. But it would be a simple matter to make something like that lasts that long using internal redundancy.

>
> --
> Rich

Fred Bloggs

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Feb 18, 2024, 9:21:55 AMFeb 18

to

On Saturday, February 17, 2024 at 10:46:47 PM UTC-5, Anthony William Sloman wrote:
> On Sunday, February 18, 2024 at 3:53:24 AM UTC+11, Fred Bloggs wrote:
> > On Friday, February 16, 2024 at 10:51:59 PM UTC-5, Anthony William Sloman wrote:
> > > On Saturday, February 17, 2024 at 5:44:50 AM UTC+11, Fred Bloggs wrote:
> > > > On Wednesday, February 14, 2024 at 10:32:40 PM UTC-5, Anthony William Sloman wrote:
> > > > > On Thursday, February 15, 2024 at 4:06:53 AM UTC+11, Fred Bloggs wrote:
> > > > > > On Wednesday, February 14, 2024 at 2:57:10 AM UTC-5, Anthony William Sloman wrote:
> > > > > > > On Wednesday, February 14, 2024 at 4:57:38 PM UTC+11, Sylvia Else wrote:
> > > <snip>
> > > > > > > Years ago it was claimed that males who survived past 80 in good health and women who survived past 85 in good health were predominantly drawn from that more robust population.
> > > > > > >
> > > > > > > It's a statistician's play ground. In practical terms you can't work out precisely what population you belong to - you might just be a lucky snowflake.
> > > > > > >
> > > > > > > An Australian male has an average life expectancy of 81.2 years, so at 81 and a couple of months I might be expected to drop dead soon. As an Australian male of 81, I've actually As an Australian male of 81, I've actually got a life expectancy of 8.44 more years.
> > > > > In reality, as an 81-year-old Australian male who never smoked and managed to get a Ph.D. I'm a member of an even longer-lived cohort, but they don't split the life expectation table finely enough that I can quote a number.
> > > > > >
> > > > > > Not really. That life statistic has a standard deviation to it. I don't know what it is for males age 72-88, but for the general population, it is 8 years. That would be 8 years either side of the mean. 2/3 of people die with a standard deviation of the mean. So that would be 33% in the range mean + 8 years. That's not called dropping dead soon.
> > > > >
> > > > > As usual, you managed to insert you comment in the middle of my paragraph,
> > > > >
> > > > > The expectation of life at a given age does tend to shrink as you get older, and the standard deviation can be expected to shrink in proportion. Your assertion assumes that the subsequent life spans will be normally distributed, and they won't be.
> > > > >
> > > > > I'm not much of a statistician, but you even worse informed.
> > > >
> > > > Your idea of being informed is a self-delusion.
> > >
> > > Perhaps, but I'm clearly better-informed than you are.
> >
> > Of course you're going to think that, it's an ego preservation refuge for the megalomaniac.
> Funny that you, of all people, would suggest that. Look in the mirror.

Using an AI retort generator now?

> > > > The density function for years of life should be normal-like, a crude fit is said to be the log-normal, the logarithm of an underlying normal variate. Literature is calling it a survival distribution, which makes sense. If F(A) is the cumulative distribution (integrated ) of that density up to year A, indicating the fraction of population still alive by year A. Then the chance of an individual of age A living to age A + T, T being time interval of continued life, should be F( A + T)- F( A ). What you're after, whether you realize it or not is the distribution of T. Literature says it's an exponential distribution, and that makes no sense at all since it implies a constant death rate. If you can't compute the mean and standard deviation of that simple thing, then you have problems.
> > > A rather long-winded way of announcing that you don't know what you are talking about.
> > I know exactly what I'm talking about. The fact of you saying it's long winded goes to show how weak is your so-called analytical thinking.
> > > > Writeup by some blithering person:
> > > >
> > > > https://users.stat.ufl.edu/~rrandles/sta4930/4930lectures/chapter2/chapter2R.pdf
> > > >
> > > > They think they're geniuses for fitting a Weibull.
> > >
> > > It's a shopping list of fitting functions. There's nothing in that write-up that shows the fit of an actual function to actual acturial data.
> >
> > It's a parameterized distribution used for fitting exponentials, and used extensively in modeling systems for reliability engineering lifetime statistics, just something else you don't know the first thing about.
> That's what I said. I don't know much about it because I've never had to do that - if my bosses wanted a more reliable system, we designed one that was more reliably by design, rather than by trrying to demonstrate that what we had was reliable enough.

Care to explain how you would design reliability into the design without knowing how to analytically analyze your proposed design?

>
> > > > Survival in general wiki:
> > > >
> > > > https://en.wikipedia.org/wiki/Survival_function.
> > >
> > > That is marginally better, in that it makes passing reference to real world breast cancer data, but it doesn't make any direct connection.
> >
> > Statistics is a tool of scientific discovery and not the science itself. Dunno what kind of childish arrested development would think it would be.
> You do seem to think exactly that.
> > > You do go to a lot of trouble to tell us that you don't know what you are talking about.
> >
> > You're too ignorant with an exacerbation of stupidity to make that assessment.
> Or so you like to think.
> > > The improved survival past age 80 for males and 85 for females might be susceptible to being modelled by a Weibull function - I wouldn't know. I could ask my cousin the statistician, but even though he is retired, I'd hate to waste his time on such a pointless question.
> >
> > It's more than just a model. It does show that beyond a critical age range the death rate becomes constant, being directly proportional to the interval of time under consideration regardless of when that interval occurs, up to a limiting age when it rapidly breaks down.
> Except that there isn't any kind of "critical age when the death rate becomes constant". The death rate depends on the environment, and it became higher when the Covid-19 virus became epidemic and went down again when most people had been vaccinated. People become more susceptible to all sorts of fatal conditions as they get older - in the US this includes not being fast enough on your feet to get away from people who have gone postal.

Another demonstration of your inability to think analytically. When confronted with externality of that nature, techniques of 'adjustment' have been developed to eliminate the craziness so as to derive the underlying constancy of whatever phenomenon it is you're studying.

The mortality in any given year, despite comprising reams of data, is conceptually a single data point. So deriving a 'baseline' as they call it will require a multitude of those data points, years. You don't just say 3 years does it like that dumb Australian government page lets on they do. The analytical way is to compute numerical variation in the data and then try to predict by how much it will corrupt the estimate of baseline. That way you end up with a range of baseline with a well-controlled confidence associated with it. Excess will be based off the mean of the resulting baseline. Maybe it takes 10 years, who knows, and nothing says the sample number stays constant.

> > You're too much of lightweight to understand any of that, so go ahead and call bullshit- the refrain of ignoramuses.
> Or in this case, the refrain of somebody who does recognise bull-shit when he sees it.

It's all relative to the use the results will be put. Your simian intellect isn't picking up on that.

>
> <snipped total waste of bandwidth on cut-and paste>
> > Total waste of time to post that, you and numbers don't get along.
> We don't cooperate. I use them - which doesn't demand anything of them except their passive existence.
> > I'm finding the business pages have the best explanations for statistical principles. They do the best job of making real sense of it. The 'nurd' pages are mostly jackass-inine factoid regurgitators. The nurds are used to being confused.
> They keep it simple for the intellectually unambitious. At Melbourne University the brighter students studied economics and the dumber ones did business studies.
> That means leaving out most of the interesting stuff.

Laughable assertion. What you call interesting is probably extraneous conversation that has nothing at all to do with actually applying the methods.

>
> --
> Bill Sloman, Sydney

Anthony William Sloman

unread,

Feb 18, 2024, 10:45:30 AMFeb 18

to

On Monday, February 19, 2024 at 1:21:55 AM UTC+11, Fred Bloggs wrote:
> On Saturday, February 17, 2024 at 10:46:47 PM UTC-5, Anthony William Sloman wrote:
> > On Sunday, February 18, 2024 at 3:53:24 AM UTC+11, Fred Bloggs wrote:
> > > On Friday, February 16, 2024 at 10:51:59 PM UTC-5, Anthony William Sloman wrote:
> > > > On Saturday, February 17, 2024 at 5:44:50 AM UTC+11, Fred Bloggs wrote:
> > > > > On Wednesday, February 14, 2024 at 10:32:40 PM UTC-5, Anthony William Sloman wrote:
> > > > > > On Thursday, February 15, 2024 at 4:06:53 AM UTC+11, Fred Bloggs wrote:
> > > > > > > On Wednesday, February 14, 2024 at 2:57:10 AM UTC-5, Anthony William Sloman wrote:
> > > > > > > > On Wednesday, February 14, 2024 at 4:57:38 PM UTC+11, Sylvia Else wrote:

<snip>

> > > > Perhaps, but I'm clearly better-informed than you are.
> > >
> > > Of course you're going to think that, it's an ego preservation refuge for the megalomaniac.
> >
> > Funny that you, of all people, would suggest that. Look in the mirror.
>
> Using an AI retort generator now?

It is a pretty obvious response - I didn't need artificial intelligence to come up with it. I've had to deal with the occasional self-satisfied half-wit from time to time, and their predictable indignation does get an appropriate response.

> > > > > The density function for years of life should be normal-like, a crude fit is said to be the log-normal, the logarithm of an underlying normal variate. Literature is calling it a survival distribution, which makes sense. If F(A) is the cumulative distribution (integrated ) of that density up to year A, indicating the fraction of population still alive by year A. Then the chance of an individual of age A living to age A + T, T being time interval of continued life, should be F( A + T)- F( A ). What you're after, whether you realize it or not is the distribution of T. Literature says it's an exponential distribution, and that makes no sense at all since it implies a constant death rate. If you can't compute the mean and standard deviation of that simple thing, then you have problems.
> > > >
> > > > A rather long-winded way of announcing that you don't know what you are talking about.
> > >
> > > I know exactly what I'm talking about. The fact of you saying it's long winded goes to show how weak is your so-called analytical thinking.

It was essentially meaningless word salad. It certainly didn't address Sylvia's question.

> > > > > Writeup by some blithering person:
> > > > >
> > > > > https://users.stat.ufl.edu/~rrandles/sta4930/4930lectures/chapter2/chapter2R.pdf
> > > > >
> > > > > They think they're geniuses for fitting a Weibull.
> > > >
> > > > It's a shopping list of fitting functions. There's nothing in that write-up that shows the fit of an actual function to actual acturial data.
> > >
> > > It's a parameterized distribution used for fitting exponentials, and used extensively in modeling systems for reliability engineering lifetime statistics, just something else you don't know the first thing about.
>
> > That's what I said. I don't know much about it because I've never had to do that - if my bosses wanted a more reliable system, we designed one that was more reliably by design, rather than by trrying to demonstrate that what we had was reliable enough.
>
> Care to explain how you would design reliability into the design without knowing how to analytically analyze your proposed design?

Easy. You avoid parts with known failure modes. Avoiding electrolytic capacitors is a good start.

Part reliability is established by observation, rather than analysis, and to get results quickly you over-stress them - run them hot and so forth. It's crude stuff, but it keeps the military happy.

> > > > > Survival in general wiki:
> > > > >
> > > > > https://en.wikipedia.org/wiki/Survival_function.
> > > >
> > > > That is marginally better, in that it makes passing reference to real world breast cancer data, but it doesn't make any direct connection.
> > >
> > > Statistics is a tool of scientific discovery and not the science itself. Dunno what kind of childish arrested development would think it would be.
> >
> > You do seem to think exactly that.
> >
> > > > You do go to a lot of trouble to tell us that you don't know what you are talking about.
> > >
> > > You're too ignorant with an exacerbation of stupidity to make that assessment.
> >
> > Or so you like to think.
> >
> > > > The improved survival past age 80 for males and 85 for females might be susceptible to being modelled by a Weibull function - I wouldn't know. I could ask my cousin the statistician, but even though he is retired, I'd hate to waste his time on such a pointless question.
> > >
> > > It's more than just a model. It does show that beyond a critical age range the death rate becomes constant, being directly proportional to the interval of time under consideration regardless of when that interval occurs, up to a limiting age when it rapidly breaks down.
> >
> > Except that there isn't any kind of "critical age when the death rate becomes constant". The death rate depends on the environment, and it became higher when the Covid-19 virus became epidemic and went down again when most people had been vaccinated. People become more susceptible to all sorts of fatal conditions as they get older - in the US this includes not being fast enough on your feet to get away from people who have gone postal.
> >
> Another demonstration of your inability to think analytically. When confronted with externality of that nature, techniques of 'adjustment' have been developed to eliminate the craziness so as to derive the underlying constancy of whatever phenomenon it is you're studying.

So you can ignore what's actually going on?

> The mortality in any given year, despite comprising reams of data, is conceptually a single data point. So deriving a 'baseline' as they call it will require a multitude of those data points, years. You don't just say 3 years does it like that dumb Australian government page lets on they do. The analytical way is to compute numerical variation in the data and then try to predict by how much it will corrupt the estimate of baseline. That way you end up with a range of baseline with a well-controlled confidence associated with it. Excess will be based off the mean of the resulting baseline. Maybe it takes 10 years, who knows, and nothing says the sample number stays constant.

The mortality in any given year is a lot of people dying for a whole range of different reasons. Declaring it to be a "single data point ignores that reality.
Taking the 3-year number as a baseline is dumb.If the three years included the Covid-19 pandemic, it's even dumber.

If you decide that everybody ought to die at precisely evenly spaced intervals, you can assess the Allan variance between the times at which they do die. Nobody sane would.

https://www.abs.gov.au/statistics/health/causes-death/provisional-mortality-statistics/latest-release

The death rate clearly varied through the years in question. More people die in winter - June to August in Australia - and it is worth thinking about why. You don't seem to want to.

> > > You're too much of lightweight to understand any of that, so go ahead and call bullshit- the refrain of ignoramuses.
> >
> > Or in this case, the refrain of somebody who does recognise bull-shit when he sees it.
>
> It's all relative to the use the results will be put. Your simian intellect isn't picking up on that.

Actuarial data is used by actuaries to calculate life insurance premiums. That's how it started, and the data is now applied to a bunch of other applications, mostly in public health, but that original application shapes most of the thinking about the subject - not yours, because you aren't thinking.

> > <snipped total waste of bandwidth on cut-and paste>

> > > Total waste of time to post that, you and numbers don't get along.
> >
> > We don't cooperate. I use them - which doesn't demand anything of them except their passive existence.
> >
> > > I'm finding the business pages have the best explanations for statistical principles. They do the best job of making real sense of it. The 'nurd' pages are mostly jackass-inine factoid regurgitators. The nurds are used to being confused.
> >
> > They keep it simple for the intellectually unambitious. At Melbourne University the brighter students studied economics and the dumber ones did business studies.
> > That means leaving out most of the interesting stuff.
>
> Laughable assertion. What you call interesting is probably extraneous conversation that has nothing at all to do with actually applying the methods.

How you you know? You haven't shown a hint of any consciousness of how the methods are applied or of the questions the statisticians might be trying to answer.

--
Bill Sloman, Sydney

a a

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Fred Bloggs

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Feb 19, 2024, 1:53:42 PMFeb 19

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You're not even close, that's not how it's done. Do you really think a responsible party developing a product for a critical application would allow a rank amateur like you to tell them everything is high reliability. Not going to happen. And there is quite a lot of analysis involved. Just because you have no idea of it doesn't mean it's not happening.

> > > > > > Survival in general wiki:
> > > > > >
> > > > > > https://en.wikipedia.org/wiki/Survival_function.
> > > > >
> > > > > That is marginally better, in that it makes passing reference to real world breast cancer data, but it doesn't make any direct connection.
> > > >
> > > > Statistics is a tool of scientific discovery and not the science itself. Dunno what kind of childish arrested development would think it would be.
> > >
> > > You do seem to think exactly that.
> > >
> > > > > You do go to a lot of trouble to tell us that you don't know what you are talking about.
> > > >
> > > > You're too ignorant with an exacerbation of stupidity to make that assessment.
> > >
> > > Or so you like to think.
> > >
> > > > > The improved survival past age 80 for males and 85 for females might be susceptible to being modelled by a Weibull function - I wouldn't know. I could ask my cousin the statistician, but even though he is retired, I'd hate to waste his time on such a pointless question.
> > > >
> > > > It's more than just a model. It does show that beyond a critical age range the death rate becomes constant, being directly proportional to the interval of time under consideration regardless of when that interval occurs, up to a limiting age when it rapidly breaks down.
> > >
> > > Except that there isn't any kind of "critical age when the death rate becomes constant". The death rate depends on the environment, and it became higher when the Covid-19 virus became epidemic and went down again when most people had been vaccinated. People become more susceptible to all sorts of fatal conditions as they get older - in the US this includes not being fast enough on your feet to get away from people who have gone postal.
> > >
> > Another demonstration of your inability to think analytically. When confronted with externality of that nature, techniques of 'adjustment' have been developed to eliminate the craziness so as to derive the underlying constancy of whatever phenomenon it is you're studying.
> So you can ignore what's actually going on?
> > The mortality in any given year, despite comprising reams of data, is conceptually a single data point. So deriving a 'baseline' as they call it will require a multitude of those data points, years. You don't just say 3 years does it like that dumb Australian government page lets on they do. The analytical way is to compute numerical variation in the data and then try to predict by how much it will corrupt the estimate of baseline. That way you end up with a range of baseline with a well-controlled confidence associated with it. Excess will be based off the mean of the resulting baseline. Maybe it takes 10 years, who knows, and nothing says the sample number stays constant.
> The mortality in any given year is a lot of people dying for a whole range of different reasons. Declaring it to be a "single data point ignores that reality.
> Taking the 3-year number as a baseline is dumb.If the three years included the Covid-19 pandemic, it's even dumber.

Actually that's what they're doing with those Australian statistics: 2019-2021 inclusive for the 2023 mortality.

>
> If you decide that everybody ought to die at precisely evenly spaced intervals, you can assess the Allan variance between the times at which they do die. Nobody sane would.

Just forget it, you're like a talking monkey in the zoo.

>
> https://www.abs.gov.au/statistics/health/causes-death/provisional-mortality-statistics/latest-release
>
> The death rate clearly varied through the years in question. More people die in winter - June to August in Australia - and it is worth thinking about why. You don't seem to want to.

The statistic of interest is the cumulative annual and not teasing out infracyclic seasonal or monthly variation. You can't keep your eye on the ball because you're confused beyond belief.

> > > > You're too much of lightweight to understand any of that, so go ahead and call bullshit- the refrain of ignoramuses.
> > >
> > > Or in this case, the refrain of somebody who does recognise bull-shit when he sees it.
> >
> > It's all relative to the use the results will be put. Your simian intellect isn't picking up on that.
> Actuarial data is used by actuaries to calculate life insurance premiums. That's how it started, and the data is now applied to a bunch of other applications, mostly in public health, but that original application shapes most of the thinking about the subject - not yours, because you aren't thinking.

Something else you have backwards. It's the public health sector and their statistics that informs the actuaries, and not the other way around.

The medical insurance is much more complex and data intensive than life insurance.

> > > <snipped total waste of bandwidth on cut-and paste>
>
> > > > Total waste of time to post that, you and numbers don't get along.
> > >
> > > We don't cooperate. I use them - which doesn't demand anything of them except their passive existence.
> > >
> > > > I'm finding the business pages have the best explanations for statistical principles. They do the best job of making real sense of it. The 'nurd' pages are mostly jackass-inine factoid regurgitators. The nurds are used to being confused.
> > >
> > > They keep it simple for the intellectually unambitious. At Melbourne University the brighter students studied economics and the dumber ones did business studies.
> > > That means leaving out most of the interesting stuff.
> >
> > Laughable assertion. What you call interesting is probably extraneous conversation that has nothing at all to do with actually applying the methods.
> How you you know? You haven't shown a hint of any consciousness of how the methods are applied or of the questions the statisticians might be trying to answer.

You're blithering. Why is this not a surprise coming from someone completely flummoxed by the prospect of using a momentary switch to turn on/off a flashlight.

>
> --
> Bill Sloman, Sydney

Sylvia Else

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Feb 19, 2024, 7:53:45 PMFeb 19

to

On 14-Feb-24 4:57 pm, Sylvia Else wrote:
> <https://www.abs.gov.au/statistics/health/causes-death/provisional-mortality-statistics/latest-release>
>
> I just need a sanity check before I raise this with the relevant agency.
> If you scroll down to "Age specific rates, 2023, 2022, Baseline", and
> look at the three right hand columns.
>

> How can the 2023 figures for each age group be less than the
> corresponding baseline average, but the all-ages number be greater than
> the baseline average?
>

> If any 2023 age group were above the baseline average, then all-ages
> number could go either way, because of different total populations in
> each age group, but with all age groups being below the baseline
> average, I just don't see it.
>
> This seems to happen not just for both sexes, but for each sex
> individually.
>
> Sylvia.

I should just add that I thought I'd try to prove this impossible using
algebra, and failed totally.

So then I used Excel, and was able to construct a counter example. So my
intuition about this was wrong. Of particular significance appears to be
that the population numbers for the base line age groups will not
generally be the same as the population numbers for the current data age
groups.

Oh well. Thanks to those who looked at this.

Sylvia.

a a

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Feb 19, 2024, 9:25:23 PMFeb 19

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The idiot Sylvia Else <syl...@email.invalid> persisting in being an Off-topic troll...

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Sylvia Else <syl...@email.invalid> wrote:

> Path: not-for-mail
> From: Sylvia Else <syl...@email.invalid>
> Newsgroups: sci.electronics.design

> Subject: Re: OT: Excess deaths stats - sanity check

> Date: Tue, 20 Feb 2024 11:53:36 +1100
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Anthony William Sloman

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Feb 19, 2024, 9:51:35 PMFeb 19

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On Tuesday, February 20, 2024 at 5:53:42 AM UTC+11, Fred Bloggs wrote:
> On Sunday, February 18, 2024 at 10:45:30 AM UTC-5, Anthony William Sloman wrote:
> > On Monday, February 19, 2024 at 1:21:55 AM UTC+11, Fred Bloggs wrote:
> > > On Saturday, February 17, 2024 at 10:46:47 PM UTC-5, Anthony William Sloman wrote:
> > > > On Sunday, February 18, 2024 at 3:53:24 AM UTC+11, Fred Bloggs wrote:
> > > > > On Friday, February 16, 2024 at 10:51:59 PM UTC-5, Anthony William Sloman wrote:
> > > > > > On Saturday, February 17, 2024 at 5:44:50 AM UTC+11, Fred Bloggs wrote:
> > > > > > > On Wednesday, February 14, 2024 at 10:32:40 PM UTC-5, Anthony William Sloman wrote:
> > > > > > > > On Thursday, February 15, 2024 at 4:06:53 AM UTC+11, Fred Bloggs wrote:
> > > > > > > > > On Wednesday, February 14, 2024 at 2:57:10 AM UTC-5, Anthony William Sloman wrote:
> > > > > > > > > > On Wednesday, February 14, 2024 at 4:57:38 PM UTC+11, Sylvia Else wrote:

<snip>

> > > > > It's a parameterized distribution used for fitting exponentials, and used extensively in modeling systems for reliability engineering lifetime statistics, just something else you don't know the first thing about.
> > >

> > > > That's what I said. I don't know much about it because I've never had to do that - if my bosses wanted a more reliable system, we designed one that was more reliably by design, rather than by trying to demonstrate that what we had was reliable enough.

> > >
> > > Care to explain how you would design reliability into the design without knowing how to analytically analyze your proposed design?
> >
> > Easy. You avoid parts with known failure modes. Avoiding electrolytic capacitors is a good start.
> >
> > Part reliability is established by observation, rather than analysis, and to get results quickly you over-stress them - run them hot and so forth. It's crude stuff, but it keeps the military happy.
>
> You're not even close, that's not how it's done. Do you really think a responsible party developing a product for a critical application would allow a rank amateur like you to tell them everything is high reliability.

Obviously not. When I was involved, the military had mickey mouse scheme for adding up the known - measured - reliabilty of components part and comboing them into an estimated reliability for the device. It was famously conservative to the point of being absurd. More sophisticated math might have done a bit better, but the military wouyldn't have trusted it.

> Not going to happen.

It's exactly what did happen.

>And there is quite a lot of analysis involved. Just because you have no idea of it doesn't mean it's not happening.

The kind of "analysis" that you specialise in - lots of hand-waving and no connection to reality.

<snip>

> > > > > It's more than just a model. It does show that beyond a critical age range the death rate becomes constant, being directly proportional to the interval of time under consideration regardless of when that interval occurs, up to a limiting age when it rapidly breaks down.
> > > >
> > > > Except that there isn't any kind of "critical age when the death rate becomes constant". The death rate depends on the environment, and it became higher when the Covid-19 virus became epidemic and went down again when most people had been vaccinated. People become more susceptible to all sorts of fatal conditions as they get older - in the US this includes not being fast enough on your feet to get away from people who have gone postal.
> > > >
> > > Another demonstration of your inability to think analytically. When confronted with externality of that nature, techniques of 'adjustment' have been developed to eliminate the craziness so as to derive the underlying constancy of whatever phenomenon it is you're studying.
> >
> > So you can ignore what's actually going on?
> > > The mortality in any given year, despite comprising reams of data, is conceptually a single data point. So deriving a 'baseline' as they call it will require a multitude of those data points, years. You don't just say 3 years does it like that dumb Australian government page lets on they do. The analytical way is to compute numerical variation in the data and then try to predict by how much it will corrupt the estimate of baseline. That way you end up with a range of baseline with a well-controlled confidence associated with it. Excess will be based off the mean of the resulting baseline. Maybe it takes 10 years, who knows, and nothing says the sample number stays constant.
> >
> > The mortality in any given year is a lot of people dying for a whole range of different reasons. Declaring it to be a "single data point ignores that reality.
> > Taking the 3-year number as a baseline is dumb.If the three years included the Covid-19 pandemic, it's even dumber.
> Actually that's what they're doing with those Australian statistics: 2019-2021 inclusive for the 2023 mortality.
> >
> > If you decide that everybody ought to die at precisely evenly spaced intervals, you can assess the Allan variance between the times at which they do die. Nobody sane would.
>
> Just forget it, you're like a talking monkey in the zoo.

Actually "to" a monkey in a zoo.

> > https://www.abs.gov.au/statistics/health/causes-death/provisional-mortality-statistics/latest-release
> >
> > The death rate clearly varied through the years in question. More people die in winter - June to August in Australia - and it is worth thinking about why. You don't seem to want to.
>
> The statistic of interest is the cumulative annual and not teasing out infracyclic seasonal or monthly variation. You can't keep your eye on the ball because you're confused beyond belief.

Nobody is interested in in how many people die in a year as such - they want to know why they died, and anything that changes the probability of an individual death is interesting, because it can give you a clue about how to delay it.

> > > > > You're too much of lightweight to understand any of that, so go ahead and call bullshit- the refrain of ignoramuses.
> > > >
> > > > Or in this case, the refrain of somebody who does recognise bull-shit when he sees it.
> > >
> > > It's all relative to the use the results will be put. Your simian intellect isn't picking up on that.
> >
> > Actuarial data is used by actuaries to calculate life insurance premiums. That's how it started, and the data is now applied to a bunch of other applications, mostly in public health, but that original application shapes most of the thinking about the subject - not yours, because you aren't thinking.
>
> Something else you have backwards. It's the public health sector and their statistics that informs the actuaries, and not the other way around.

You clearly don't know the history of the field

> The medical insurance is much more complex and data intensive than life insurance.

It is now, because it pays for interventions that can delay death, by keeping the patient alive (and paying their premiums) until they can die of something else.

<snip>

> > How you you know? You haven't shown a hint of any consciousness of how the methods are applied or of the questions the statisticians might be trying to answer.
>
> You're blithering. Why is this not a surprise coming from someone completely flummoxed by the prospect of using a momentary switch to turn on/off a flashlight.

Scarcely flummoxed. You seemed to be unaware that mechanical switches tend to bounce, so you can get several switch closures when you activate them and have to design the electronics to cope with that. I spelled it out at the time, so it seems to be one of those points of detail - like the difference between "highly conserved" and "doesn't mutate" - that you can't get your head around.

--
Bill Sloman, Sydney

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