Statistika For Life

Rabu, 21 November 2007

15 new messages in 7 topics - digest

sci.stat.math
http://groups.google.com/group/sci.stat.math?hl=en

sci.stat.math@googlegroups.com

Today's topics:

* wrong R-Squared value?? - 2 messages, 2 authors
http://groups.google.com/group/sci.stat.math/browse_thread/thread/259e11ac412a3219?hl=en
* The only thing THAT MATTERS - 3 messages, 2 authors
http://groups.google.com/group/sci.stat.math/browse_thread/thread/db29382ab441eab8?hl=en
* Turn a uniform number to normal random numbers - 1 messages, 1 author
http://groups.google.com/group/sci.stat.math/browse_thread/thread/054d911605199f7c?hl=en
* Sample Size - 4 messages, 2 authors
http://groups.google.com/group/sci.stat.math/browse_thread/thread/ff810bbd9cf63993?hl=en
* complete sufficient = minimal sufficient? - 1 messages, 1 author
http://groups.google.com/group/sci.stat.math/browse_thread/thread/05c766fa3c882185?hl=en
* Time Series Analysis Help - 3 messages, 2 authors
http://groups.google.com/group/sci.stat.math/browse_thread/thread/dd67a6fca7d0207e?hl=en
* Statistical Methods for Ranks? - 1 messages, 1 author
http://groups.google.com/group/sci.stat.math/browse_thread/thread/d02d4f899aae90fe?hl=en

==============================================================================
TOPIC: wrong R-Squared value??
http://groups.google.com/group/sci.stat.math/browse_thread/thread/259e11ac412a3219?hl=en
==============================================================================

== 1 of 2 ==
Date: Tues, Nov 20 2007 2:29 am
From: jantunes

> For a time series, the obvious spurious correlations
> involve simple linear trends in separate variables.
> Or cycles.
> If two variables separately have a similar trend,
> they will have a positive correlation.
>
> If Sequence-number correlates with your raw data, you
> have a potential problem.

> > I'm trying to predict the resource usage for a
> > given computer task (x = number of times the taks
> > is repeated). So, I get a different y and y'
> > (prediction) for a different type of resource (CPU,
> > memory, etc).
>
> This *sounds* like a matter of bench marking. For
> that, the "time series" aspect should be incidental
> and irrelevant.
> Each separate "experiment" should give the same
> results regardless of when it is run. Is there *any*
> sort of proper carry-over between experiments?
>
> One really onerous way that these data could resemble
> a time series is if you recorded the x and y as
> cumulative counters, and never subtracted in order to
> find the data for the separate experiments.
>
> That would create a strong correlation that is
> "spurious" and essentially useless.
>
> Why is there any carry-over between experiments?

There is no separate experiment. This is a one experiment only, which consists in repeating the same task (e.g., a client/server request) several times.
But yes, there is a natural cumulative data because I'm measuring the current resource usage of the application (memory, disk, etc).

Thanks

== 2 of 2 ==
Date: Tues, Nov 20 2007 1:35 pm
From: Richard Ulrich

On Tue, 20 Nov 2007 05:29:06 EST, jantunes <jasantunes@gmail.com>
wrote:

[snip]
RU > >
> > This *sounds* like a matter of bench marking. For
> > that, the "time series" aspect should be incidental
> > and irrelevant.

Okay, for computer server benchmarks, the background
information might predict the speed of response. Response
will be faster when you are not competing with a couple of
users who are downloading movies, for example.

RU > >
> > Each separate "experiment" should give the same
> > results regardless of when it is run. Is there *any*
> > sort of proper carry-over between experiments?
> >
> > One really onerous way that these data could resemble
> > a time series is if you recorded the x and y as
> > cumulative counters, and never subtracted in order to
> > find the data for the separate experiments.
> >
> > That would create a strong correlation that is
> > "spurious" and essentially useless.
> >
> > Why is there any carry-over between experiments?
>
ja >
> There is no separate experiment. This is a one experiment only,
> which consists in repeating the same task (e.g., a client/server
> request) several times.

Why is it not true that each measurement is a separate experiment,
with its own separate timing?

Why is your application different from every other bench marking
I've read of?

> But yes, there is a natural cumulative data because I'm measuring
> the current resource usage of the application (memory, disk, etc).

SEE what I wrote last time, above.

--
Rich Ulrich, wpilib@pitt.edu

http://www.pitt.edu/~wpilib/index.html

==============================================================================
TOPIC: The only thing THAT MATTERS
http://groups.google.com/group/sci.stat.math/browse_thread/thread/db29382ab441eab8?hl=en
==============================================================================

== 1 of 3 ==
Date: Tues, Nov 20 2007 6:46 am
From: "Luis A. Afonso"

19, 2007 9:45 PM
Author: John Smith
Subject: Re: The only thing THAT MATTERS

Nobody should believe anything Afonso writes until he answers these simple questions. When a Monte Carlo created distribution is created, are the 1% and 99% percentiles statistics or parameters? Bonus question: what is the role of the parameter in a Monte Carlo? John ****

MY RESPONSE

Who believes in such a person that is not an author of a paper concerning Monte Carlo Method?
Who believes someone that did not learn that don't know that the Box-Muller transformation is a rigorous way to get normal values?
How credible is a person that ignores the Conovers Textbook Practical Nonparametric Statistics where one learns how to obtain the Cumulative Frequencies from data?
What to say from a so-called statistician that never met the 1956 Dvorestky- Kiefer- Wolfovitz inequality that teach us how a Empirical Distribution Function is close to the Theoretical one?
How can we trust a person that is ZERO AWARE from thousands papers where Monte Carlo has been used to confirm or deny approach results the Theory provide?

What to say from a person that ask inappropriate questions about a simple DISTANCE (which is the realm of the Lilliefors-Kolmogorov-Smirnov test on normality'

When the samples are created,
In a Monte Carlo Simulation,
One gets the first step merely,
A preliminary point, a situation
To have the Test Distribution.

This one is then constructed
In a way we want, no exception,
It's elementary, do you see?
Only imbeciles find confusion,
No trouble for true statistician.

Lilliefors test, not excluded
From this clear classification,
Test is only a DISTANCE simply
Any sample how far measuring
The model: what other thing?

The Distance by two guys founded:
Kolmogorov-Smirnov celebration.
Who are able, really, to fight me?
Nobody except an idiot boring
Full addicted in talk rambling.

His litany has any real reaching:
Statistics or Parameters is asking
Not at all, neither, exclusively,
A DISTANCE, not more, clearly.

Who believes some Smith boys,
So much ignorant in this matter
Never Monte Carlo got annoys?
How much Empirical, how better,
Can approach Distribution Function
Should, sure, DKW have instruction!
Such a thing he yet met never.

I've not time to spend, surely,
View so great, asinine, opacity.

******
Luis Amaral Afonso

== 2 of 3 ==
Date: Tues, Nov 20 2007 10:48 am
From: John Smith

See how Alfonso responds to a simple statistics question with lots of nonsense? He is trying to hide the fact that he cannot answer the simple question, because he doesn't know any statistics.

John

== 3 of 3 ==
Date: Tues, Nov 20 2007 11:18 am
From: "Luis A. Afonso"

John Smith

Are you saying, IGNORANT JACK, that the Test Statistics of the Lilliefors (Kolmogorov - Smirnov) Test of Normality IS NOT A DISTANCE and trough Monte Carlo one are not able to get he Critical Values?
What paper are you authoring in this area (in a decent Journal) say to us, the Readers?
Don't you feel ridiculous?

***

Luis Amaral Afonso

==============================================================================
TOPIC: Turn a uniform number to normal random numbers
http://groups.google.com/group/sci.stat.math/browse_thread/thread/054d911605199f7c?hl=en
==============================================================================

== 1 of 1 ==
Date: Tues, Nov 20 2007 8:12 am
From: "Luis A. Afonso"

David

FACTS ARE FACTS

My post dated Nov 14, 2007 2:05 PM (presented in this thread) a solution that is more general than yours Nov 19, 2007 11.28 AM one.
I DIDN´T say you copied me: only that your post is useless.
Furthermore I identified the algorithm, Box- Muller's, an useful information to the OP for ulterior self checking.

Be aware: those that prefer OPPINIONS to FACTS are incapable to perform whatsoever in SCIENCE.

****
Luis Amaral Afonso

==============================================================================
TOPIC: Sample Size
http://groups.google.com/group/sci.stat.math/browse_thread/thread/ff810bbd9cf63993?hl=en
==============================================================================

== 1 of 4 ==
Date: Tues, Nov 20 2007 12:19 pm
From: John Smith

Luisa,

Still can't answer a simple question, can you?

John

PS -- Have someone who knows English read posts by myself and by Tomsky. Only a moron would mistake the writings styles but, guess what?

== 2 of 4 ==
Date: Tues, Nov 20 2007 12:29 pm
From: "Luis A. Afonso"

N(0,1)

N(0,2)

N(0,3)

**** Date: Nov 20, 2007 3:19 PM
Author: John Smith
Subject: Re: Sample Size

Luisa,

Still can't answer a simple question, can you?

John

PS -- Have someone who knows English read posts by myself and by Tomsky. Only a moron would mistake the writings styles but, guess what?****

Jean, Joan

In what concern STUPIDITY I found no difference between you and Jackie, Jacqueline.
****

Luis Amaral Afonso

== 3 of 4 ==
Date: Tues, Nov 20 2007 12:30 pm
From: "Luis A. Afonso"

**** Date: Nov 20, 2007 3:19 PM
Author: John Smith
Subject: Re: Sample Size

Luisa,

Still can't answer a simple question, can you?

John

PS -- Have someone who knows English read posts by myself and by Tomsky. Only a moron would mistake the writings styles but, guess what?****

Jean, Joan

In what concern STUPIDITY I found no difference between you and Jackie, Jacqueline.
****

Luis Amaral Afonso

== 4 of 4 ==
Date: Tues, Nov 20 2007 1:17 pm
From: John Smith

Luisa,

I wrote:
PS -- Have someone who knows English read posts by myself and by Tomsky. Only a moron would mistake the writings styles but, guess what?****

you wrote: In what concern STUPIDITY I found no difference between you and Jackie, Jacqueline.
****

It's obvious you can't answer a simple statistics question, but can't you follow instructions? I said "some who knows English"; that obviously excludes you.

John

==============================================================================
TOPIC: complete sufficient = minimal sufficient?
http://groups.google.com/group/sci.stat.math/browse_thread/thread/05c766fa3c882185?hl=en
==============================================================================

== 1 of 1 ==
Date: Tues, Nov 20 2007 1:08 pm
From: leading

1. As any statistics textbook points out, a complete sufficient
statistic is necessarily minimal sufficient.
Conversely is minimal sufficient statistic also complete sufficient?
2. If G is a complete sufficient statistic, and f is a function such
that f(G) is a sufficient statistic, is f(G) also complete?
Thanks

==============================================================================
TOPIC: Time Series Analysis Help
http://groups.google.com/group/sci.stat.math/browse_thread/thread/dd67a6fca7d0207e?hl=en
==============================================================================

== 1 of 3 ==
Date: Tues, Nov 20 2007 3:24 pm
From: Idgarad

Ok I am not a statistics guru I admit but I have trying to do some
basic forecasting that would meeting some basic statistical
requirements. I have the following data:

Date MIPS
1/5/2004 306.203
1/12/2004 364.29
1/19/2004 384.779
1/26/2004 387.91
2/2/2004 339.041
2/9/2004 414.383
2/16/2004 313.764
2/23/2004 335.001
3/1/2004 323.978
3/8/2004 312.729
3/15/2004 343.589
3/22/2004 333.252
3/29/2004 376.878
4/5/2004 390.825
4/12/2004 356.892
4/19/2004 383.517
4/26/2004 325.227
5/3/2004 254.279
5/10/2004 255.221
5/17/2004 266.575
5/24/2004 270.073
5/31/2004 293.269
6/7/2004 309.114
6/14/2004 311.633
6/21/2004 350.444
6/28/2004 296.203
7/5/2004 332.153
7/12/2004 306.23
7/19/2004 368.466
7/26/2004 334.271
8/2/2004 349.002
8/9/2004 378.682
8/16/2004 333.731
8/23/2004 380.037
8/30/2004 298.417
9/6/2004 288.728
9/13/2004 342.81
9/20/2004 382.866
9/27/2004 419.828
10/4/2004 379.289
10/11/2004 400.749
10/18/2004 453.514
10/25/2004 388.742
11/1/2004 333.935
11/8/2004 341.659
11/15/2004 281.586
11/22/2004 305.749
11/29/2004 310.391
12/6/2004 317.704
12/13/2004 380.804
12/20/2004 319.389
12/27/2004 361.442
1/3/2005 369.1764612
1/10/2005 416.6238169
1/17/2005 459.5359423
1/24/2005 365.4009445
1/31/2005 413.3630776
2/7/2005 291.3910135
2/14/2005 305.105
2/21/2005 464.8482752
2/28/2005 363.0336105
3/7/2005 264.7677899
3/14/2005 344.880868
3/21/2005 325.8519595
3/28/2005 321.1775701
4/4/2005 404.5693965
4/11/2005 392.0416371
4/18/2005 430.7946661
4/25/2005 427.1631644
5/2/2005 411.8648374
5/9/2005 386.8547968
5/16/2005 383.4840298
5/23/2005 381.5493873
5/30/2005 315.0086187
6/6/2005 354.5324168
6/13/2005 327.772
6/20/2005 369.0157653
6/27/2005 408.0830566
7/4/2005 434.5275972
7/11/2005 371.5106324
7/18/2005 408.1991382
7/25/2005 405.0429881
8/1/2005 373.8240641
8/8/2005 364.0034462
8/15/2005 369.6471424
8/22/2005 382.0108071
8/29/2005 410.7909099
9/5/2005 330.9051756
9/12/2005 368.7685134
9/19/2005 270.4893379
9/26/2005 404.0606091
10/3/2005 383.8872826
10/10/2005 466.5515718
10/17/2005 486.673
10/24/2005 448.0580021
10/31/2005 373.5319544
11/7/2005 358.4208151
11/14/2005 398.9761027
11/21/2005 318.3299946
11/28/2005 358.0366431
12/5/2005 344.9174087
12/12/2005 386.8313941
12/19/2005 294.1100542
12/26/2005 293.881162
1/2/2006 433.7141952
1/9/2006 476.274226
1/16/2006 475.7067041
1/23/2006 459.1203218
1/30/2006 361.2039406
2/6/2006 363.7221527
2/13/2006 380.1952852
2/20/2006 442.1721436
2/27/2006 357.9469694
3/6/2006 395.7442366
3/13/2006 450.9923943
3/20/2006 367.7855186
3/27/2006 402.778072
4/3/2006 493.4095257
4/10/2006 493.468
4/17/2006 469.1306141
4/24/2006 450.0128534
5/1/2006 442.5117675
5/8/2006 428.8031172
5/15/2006 470.2158386
5/22/2006 446.2431756
5/29/2006 317.8183222
6/5/2006 369.3162037
6/12/2006 410.4558021
6/19/2006 443.1421911
6/26/2006 397.1971946
7/3/2006 481.3922888
7/10/2006 525.2947246
7/17/2006 473.5077361
7/24/2006 517.5520329
7/31/2006 466.9906984
8/7/2006 431.1475016
8/14/2006 399.5471642
8/21/2006 440.8823488
8/28/2006 439.6991779
9/4/2006 362.8644597
9/11/2006 406.762618
9/18/2006 363.0828509
9/25/2006 491.8909378
10/2/2006 527.5336233
10/9/2006 516.9000381
10/16/2006 554.2020878
10/23/2006 650.9110702
10/30/2006 527.429268
11/6/2006 520.5231633
11/13/2006 419.1709031
11/20/2006 441.3769311
11/27/2006 407.7421329
12/4/2006 423.0796675
12/11/2006 541.489909
12/18/2006 395.1153918
12/25/2006 407.3078582
1/1/2007 555.9770864
1/8/2007 484.9516878
1/15/2007 554.6924101
1/22/2007 547.1910996
1/29/2007 498.570364
2/5/2007 532.9759432
2/12/2007 432.4194752
2/19/2007 497.8181418
2/26/2007 407.4818148
3/5/2007 463.2326725
3/12/2007 547.1052888
3/19/2007 499.1447529
3/26/2007 441.1002226
4/2/2007 435.5250358
4/9/2007 510.0561347
4/16/2007 460.6838179
4/23/2007 508.6014031
4/30/2007 514.7918906
5/7/2007 506.1699276
5/14/2007 538.0826675
5/21/2007 497.6096175
5/28/2007 434.4788358
6/4/2007 528.1184467
6/11/2007 432.9866137
6/18/2007 510.1264458
6/25/2007 487.4279266
7/2/2007 495.274668
7/9/2007 508.7542205
7/16/2007 572.8591187
7/23/2007 657.6611519
7/30/2007 594.0857848
8/6/2007 590.5344634
8/13/2007 604.0715949
8/20/2007 533.396821
8/27/2007 498.3182266
9/3/2007 491.3865539
9/10/2007 548.296464
9/17/2007 459.3107549
9/24/2007 543.1050647

That data is weekly usage of a system. I have done what research I
have and done some basic forecasting comparing previous year and doing
forecasts based on that. I am trying to find a more accurate way to
forecast this and my research has brought me to the ARIMA method for
looking at seasonal data.

Pouring through that resources I have I have found Gretl as a
potential tool. I need to generate a forecast up to 24 weeks in
advance. But I am at a loss. Each time I try, to the best of my
ability to process a forecast I am not getting any results that are
realistic due to my lack of statistical knowledge and a poor
understanding of most statistical software (Gretl included.) I keep
coming back to ARIMA(0,1,1)(0,1,1) with a seasonal period of 12 weeks.
I know this to be wrong but without a strong math background (I am a
technical guru, not a statistical guru) and I have hit a brick wall.

Can someone help explain what I need to do, using Gretl or some
similar tool in how to do accurate forecasting based on the above
data. I need to repeat this process weekly.

The activity is roughly quarterly but there is some drift on when a
quarter starts and ends (by up to two weeks either direction) so ARIMA
seemed to be the best method for forecasting.

Help!

== 2 of 3 ==
Date: Tues, Nov 20 2007 5:33 pm
From: dave@autobox.com

On Nov 20, 6:24 pm, Idgarad <idga...@gmail.com> wrote:
> Ok I am not a statistics guru I admit but I have trying to do some
> basic forecasting that would meeting some basic statistical
> requirements. I have the following data:
>
> Date MIPS
> 1/5/2004 306.203
> 1/12/2004 364.29
> 1/19/2004 384.779
> 1/26/2004 387.91
> 2/2/2004 339.041
> 2/9/2004 414.383
> 2/16/2004 313.764
> 2/23/2004 335.001
> 3/1/2004 323.978
> 3/8/2004 312.729
> 3/15/2004 343.589
> 3/22/2004 333.252
> 3/29/2004 376.878
> 4/5/2004 390.825
> 4/12/2004 356.892
> 4/19/2004 383.517
> 4/26/2004 325.227
> 5/3/2004 254.279
> 5/10/2004 255.221
> 5/17/2004 266.575
> 5/24/2004 270.073
> 5/31/2004 293.269
> 6/7/2004 309.114
> 6/14/2004 311.633
> 6/21/2004 350.444
> 6/28/2004 296.203
> 7/5/2004 332.153
> 7/12/2004 306.23
> 7/19/2004 368.466
> 7/26/2004 334.271
> 8/2/2004 349.002
> 8/9/2004 378.682
> 8/16/2004 333.731
> 8/23/2004 380.037
> 8/30/2004 298.417
> 9/6/2004 288.728
> 9/13/2004 342.81
> 9/20/2004 382.866
> 9/27/2004 419.828
> 10/4/2004 379.289
> 10/11/2004 400.749
> 10/18/2004 453.514
> 10/25/2004 388.742
> 11/1/2004 333.935
> 11/8/2004 341.659
> 11/15/2004 281.586
> 11/22/2004 305.749
> 11/29/2004 310.391
> 12/6/2004 317.704
> 12/13/2004 380.804
> 12/20/2004 319.389
> 12/27/2004 361.442
> 1/3/2005 369.1764612
> 1/10/2005 416.6238169
> 1/17/2005 459.5359423
> 1/24/2005 365.4009445
> 1/31/2005 413.3630776
> 2/7/2005 291.3910135
> 2/14/2005 305.105
> 2/21/2005 464.8482752
> 2/28/2005 363.0336105
> 3/7/2005 264.7677899
> 3/14/2005 344.880868
> 3/21/2005 325.8519595
> 3/28/2005 321.1775701
> 4/4/2005 404.5693965
> 4/11/2005 392.0416371
> 4/18/2005 430.7946661
> 4/25/2005 427.1631644
> 5/2/2005 411.8648374
> 5/9/2005 386.8547968
> 5/16/2005 383.4840298
> 5/23/2005 381.5493873
> 5/30/2005 315.0086187
> 6/6/2005 354.5324168
> 6/13/2005 327.772
> 6/20/2005 369.0157653
> 6/27/2005 408.0830566
> 7/4/2005 434.5275972
> 7/11/2005 371.5106324
> 7/18/2005 408.1991382
> 7/25/2005 405.0429881
> 8/1/2005 373.8240641
> 8/8/2005 364.0034462
> 8/15/2005 369.6471424
> 8/22/2005 382.0108071
> 8/29/2005 410.7909099
> 9/5/2005 330.9051756
> 9/12/2005 368.7685134
> 9/19/2005 270.4893379
> 9/26/2005 404.0606091
> 10/3/2005 383.8872826
> 10/10/2005 466.5515718
> 10/17/2005 486.673
> 10/24/2005 448.0580021
> 10/31/2005 373.5319544
> 11/7/2005 358.4208151
> 11/14/2005 398.9761027
> 11/21/2005 318.3299946
> 11/28/2005 358.0366431
> 12/5/2005 344.9174087
> 12/12/2005 386.8313941
> 12/19/2005 294.1100542
> 12/26/2005 293.881162
> 1/2/2006 433.7141952
> 1/9/2006 476.274226
> 1/16/2006 475.7067041
> 1/23/2006 459.1203218
> 1/30/2006 361.2039406
> 2/6/2006 363.7221527
> 2/13/2006 380.1952852
> 2/20/2006 442.1721436
> 2/27/2006 357.9469694
> 3/6/2006 395.7442366
> 3/13/2006 450.9923943
> 3/20/2006 367.7855186
> 3/27/2006 402.778072
> 4/3/2006 493.4095257
> 4/10/2006 493.468
> 4/17/2006 469.1306141
> 4/24/2006 450.0128534
> 5/1/2006 442.5117675
> 5/8/2006 428.8031172
> 5/15/2006 470.2158386
> 5/22/2006 446.2431756
> 5/29/2006 317.8183222
> 6/5/2006 369.3162037
> 6/12/2006 410.4558021
> 6/19/2006 443.1421911
> 6/26/2006 397.1971946
> 7/3/2006 481.3922888
> 7/10/2006 525.2947246
> 7/17/2006 473.5077361
> 7/24/2006 517.5520329
> 7/31/2006 466.9906984
> 8/7/2006 431.1475016
> 8/14/2006 399.5471642
> 8/21/2006 440.8823488
> 8/28/2006 439.6991779
> 9/4/2006 362.8644597
> 9/11/2006 406.762618
> 9/18/2006 363.0828509
> 9/25/2006 491.8909378
> 10/2/2006 527.5336233
> 10/9/2006 516.9000381
> 10/16/2006 554.2020878
> 10/23/2006 650.9110702
> 10/30/2006 527.429268
> 11/6/2006 520.5231633
> 11/13/2006 419.1709031
> 11/20/2006 441.3769311
> 11/27/2006 407.7421329
> 12/4/2006 423.0796675
> 12/11/2006 541.489909
> 12/18/2006 395.1153918
> 12/25/2006 407.3078582
> 1/1/2007 555.9770864
> 1/8/2007 484.9516878
> 1/15/2007 554.6924101
> 1/22/2007 547.1910996
> 1/29/2007 498.570364
> 2/5/2007 532.9759432
> 2/12/2007 432.4194752
> 2/19/2007 497.8181418
> 2/26/2007 407.4818148
> 3/5/2007 463.2326725
> 3/12/2007 547.1052888
> 3/19/2007 499.1447529
> 3/26/2007 441.1002226
> 4/2/2007 435.5250358
> 4/9/2007 510.0561347
> 4/16/2007 460.6838179
> 4/23/2007 508.6014031
> 4/30/2007 514.7918906
> 5/7/2007 506.1699276
> 5/14/2007 538.0826675
> 5/21/2007 497.6096175
> 5/28/2007 434.4788358
> 6/4/2007 528.1184467
> 6/11/2007 432.9866137
> 6/18/2007 510.1264458
> 6/25/2007 487.4279266
> 7/2/2007 495.274668
> 7/9/2007 508.7542205
> 7/16/2007 572.8591187
> 7/23/2007 657.6611519
> 7/30/2007 594.0857848
> 8/6/2007 590.5344634
> 8/13/2007 604.0715949
> 8/20/2007 533.396821
> 8/27/2007 498.3182266
> 9/3/2007 491.3865539
> 9/10/2007 548.296464
> 9/17/2007 459.3107549
> 9/24/2007 543.1050647
>
> That data is weekly usage of a system. I have done what research I
> have and done some basic forecasting comparing previous year and doing
> forecasts based on that. I am trying to find a more accurate way to
> forecast this and my research has brought me to the ARIMA method for
> looking at seasonal data.
>
> Pouring through that resources I have I have found Gretl as a
> potential tool. I need to generate a forecast up to 24 weeks in
> advance. But I am at a loss. Each time I try, to the best of my
> ability to process a forecast I am not getting any results that are
> realistic due to my lack of statistical knowledge and a poor
> understanding of most statistical software (Gretl included.) I keep
> coming back to ARIMA(0,1,1)(0,1,1) with a seasonal period of 12 weeks.
> I know this to be wrong but without a strong math background (I am a
> technical guru, not a statistical guru) and I have hit a brick wall.
>
> Can someone help explain what I need to do, using Gretl or some
> similar tool in how to do accurate forecasting based on the above
> data. I need to repeat this process weekly.
>
> The activity is roughly quarterly but there is some drift on when a
> quarter starts and ends (by up to two weeks either direction) so ARIMA
> seemed to be the best method for forecasting.
>
> Help!

Idgarad,

Please review http://www.autobox.com/idgarad and find some output from
AUTOBOX.

http://www.autobox.com/idgarad/accff.jpg

You will find in this case

1. There are significant level shifts at time point 65 and 114 ...both
to the upside ...NO TREND HERE ...just two level shifts.
2. There are a number of anomalous observations which need to be
accomodated so that they don't mask the model.
3. A number of Holidays are important.
4. There is a strong week of the year effect.
5. the ARIMA MODEL is simply a (1,1)

[(1- .746B** 1)]**-1 [(1- .276B** 1)]

At this juncture you can simply buy AUTOBOX or some similar commercial
program or simply program the following

a. Detect simultaneously the presence of

Pulses, Level Shifts, Seasonal Pulses , Local Time Trends

The point(s)in time where the parameters of the model may have
changed suggesting too much data

The form of the SARIMA MODEL

Any needed transformations to homogeneize the variance of the
errors

What Holiday indicators are important and what the temporal
response is to each ( viz. contemporaneous , lag , lead )

What weeks of the year are important.

Pursue all of these until the plot of your residuals looks like

http://www.autobox.com/idgarad/res.jpg which suggests that the signal
has been removed from the data

http://www.autobox.com/idgarad/actfore.jpg

The R-Squared for the final model was 86.5%

There are a number of success stories on our web site regarding daily
and weekly predictive models.

If I can help please give me a call.

Dave Reilly

== 3 of 3 ==
Date: Tues, Nov 20 2007 5:53 pm
From: dave@autobox.com

idgarad,

Please review http://www.autobox.com/idgarad

and note that a reasonable weekly model yielding an r_squared of 86%
can be accomplished by programming
a procedure to detect level shifts and local time trends
the importance of a number of possible holidays
tests for constancy of parameters over time
tests for homogeneity of variance of the errors

http://www.autobox.com/idgarad/ab50pro.123
http://www.autobox.com/idgarad/accff.jpg
http://www.autobox.com/idgarad/actfore.jpg
http://www.autobox.com/idgarad/actres.jpg
http://www.autobox.com/idgarad/res.jpg
http://www.autobox.com/idgarad/fore.jpg
http://www.autobox.com/idgarad/stat.htm
http://www.autobox.com/idgarad/model.bmp
http://www.autobox.com/idgarad/verbal.txt

You can try it out by downloading the FREEWARE VERSION of AUTOBOX
called FREEFORE

http://www.autobox.com/freef.exe

Just form your data like http://www.autobox.com/idgarad/idgard.asc

and you should be able to run the free software each week ...develop a
model automatically ...and even get your 1 week ahead forecast all
without charge.

Hope this helps

Dave Reilly
Automatic Forecasting Systems
http://www.autobox.com
215-675-0652

==============================================================================
TOPIC: Statistical Methods for Ranks?
http://groups.google.com/group/sci.stat.math/browse_thread/thread/d02d4f899aae90fe?hl=en
==============================================================================

== 1 of 1 ==
Date: Tues, Nov 20 2007 9:25 pm
From: mprocopio@gmail.com

I have made some progress; I am able to apply Spearman's Rank
Correlation Test (using the SPSS implementation) to determine pairwise
"closeness" of, say, two different metrics. This is a hypothesis test
at a given confidence level(with associated critical value), and I can
determine independence from this manner. If not independent, I think
it's fair to say that the metrics are "measuring the same thing".

Here's one thought. Is it principled to take the average over ALL
frames in ALL datasets:

Dataset DS_ALL_AVG

Metric M1 M2 M3
Alg

Alg. A x x x
Alg. B x x x
Alg. C x x x
Alg. D x x x
Alg. E x x x

Importantly, I am not obfuscating the result by combining metrics, but
I still get an overall answer and ranking.

So one final question is: Consider that the rankings of the algorithms
M1, M2, and M3 are SIMILAR but NOT IDENTICAL. The Spearman test may or
may not give a statistical basis to say that they're independent. Even
if they are NOT independent--how do you obtain a final ranking of the
algorithms?

The other tricky part is applying the rank-oriented tests when your
values are means with confidence intervals, and the confidence
intervals overlap.

==============================================================================

You received this message because you are subscribed to the Google Groups "sci.stat.math"
group.

To post to this group, visit http://groups.google.com/group/sci.stat.math?hl=en

To unsubscribe from this group, send email to sci.stat.math-unsubscribe@googlegroups.com

To change the way you get mail from this group, visit:
http://groups.google.com/group/sci.stat.math/subscribe?hl=en

To report abuse, send email explaining the problem to abuse@googlegroups.com

==============================================================================
Google Groups: http://groups.google.com?hl=en

Selasa, 20 November 2007

20 new messages in 11 topics - digest

sci.stat.math
http://groups.google.com/group/sci.stat.math?hl=en

sci.stat.math@googlegroups.com

Today's topics:

==============================================================================
TOPIC: Probability modelling question
http://groups.google.com/group/sci.stat.math/browse_thread/thread/01e1e906ea8c5cc2?hl=en
==============================================================================

== 1 of 1 ==
Date: Mon, Nov 19 2007 1:06 am
From: "deepa.bharti@gmail.com"

On Nov 18, 10:08 pm, richardsta...@comcast.net wrote:
> On Sun, 18 Nov 2007 11:46:40 +0000, Claire Blair <no.re...@here.com>
> wrote:
>
> >I have a basic question, which I hope someone can answer for me.
>
> >I have sample data that consists of binary (Yes/No) data. The Yes's
> >correspond to a 'success' event, and the No's correspond to a 'Failure'
> >event.
>
> >I want to know how I can use the data to model and predict the
> >probability of a success event.
>
> >I think the logistic model is what I should use, but I am not sure.
>
> >Please advise.
>
> Assuming that by "model" you mean you have explanatory variables that
> you think help predict success or failure, then yes, a logistic model
> (or a probit) is appropriate.
> -Dick Startz

I think u can do a run test which gives u the probability u are luking
for

==============================================================================
TOPIC: Instructors' manuals for Engineering books
http://groups.google.com/group/sci.stat.math/browse_thread/thread/2e9bf83439a6796b?hl=en
==============================================================================

== 1 of 1 ==
Date: Mon, Nov 19 2007 2:03 am
From: timminny

I really need the Solutions manual for Electric Machinery, 6th Ed.,
Fitzgerald,
Kingsley, Umans

Would you please send me that one
My email is tim.minny@gmail.com
Thanks for your kindness

==============================================================================
TOPIC: BOOBS SEX VIDEO FULL DOWNLOAD FREE WORLD
SEX WEB SITE FREE

http://groups.google.com/group/sci.stat.math/browse_thread/thread/c051d80745cbeeda?hl=en
==============================================================================

== 1 of 1 ==
Date: Mon, Nov 19 2007 5:28 am
From: yyu

BOOBS SEX VIDEO FULL DOWNLOAD FREE
WORLD SEX WEB SITE FREE

REYAL SEX ONLY FUCKING SEX

INDIAN GIRLS SEX IN YOUR PLACE

TO NIGHT SEX VIDEO FREE

BooBS SEX VIDEO FREE

http://SEX-TIME-Show-FUCKING.notlong.com

==============================================================================
TOPIC: cofficient curve-help
http://groups.google.com/group/sci.stat.math/browse_thread/thread/dc10a91401429eb0?hl=en
==============================================================================

== 1 of 2 ==
Date: Mon, Nov 19 2007 7:13 am
From: David Winsemius

lior <liordp2006@gmail.com> wrote in
news:27281965.1195398858790.JavaMail.jakarta@nitrogen.mathforum.org:

> hi
>
> i have a problem to find the cofficient curve by using MATLAB when
> there is no short sale

There are difficulties in rigging a sailing vessel for high seas without a
storm jib.

> (i cant use excel because my data is so big that i cant find the
> inverse matrix)

Matlab won't help much. either, when you are trying to sail a constant
course. Salt water interacts negatively with the hardware.

--
David Winsemius

== 2 of 2 ==
Date: Mon, Nov 19 2007 8:12 am
From: Robert Dodier

David Winsemius wrote:

> > i have a problem to find the cofficient curve by using MATLAB when
> > there is no short sale
>
> There are difficulties in rigging a sailing vessel for high seas without a
> storm jib.
>
> > (i cant use excel because my data is so big that i cant find the
> > inverse matrix)
>
> Matlab won't help much. either, when you are trying to sail a constant
> course. Salt water interacts negatively with the hardware.

You rock, dude. Thanks for the informative post. Keep up the good
work.

Robert Dodier

==============================================================================
TOPIC: Linear Model Problem
http://groups.google.com/group/sci.stat.math/browse_thread/thread/cbdae4ed0080ff7a?hl=en
==============================================================================

== 1 of 1 ==
Date: Mon, Nov 19 2007 7:27 am
From: sonicb11

On Nov 18, 2:07 am, "Anon." <bob.oh...@helsinki.fi> wrote:
> sonicb11 wrote:
> > On Nov 17, 8:43 pm, Richard Ulrich <Rich.Ulr...@comcast.net> wrote:
> >> On Thu, 15 Nov 2007 08:42:11 -0800 (PST), sonicb11
>
> >> <williamp...@hotmail.com> wrote:
> >>> I am working on a linear model problem.
> >>> Here is the model:
> >>> Wi = a*Zi + b*Zi*Xi + Zi*Ei
> >> That's a meaningful model? That has Zi in each term.
> >> Is there a typographical error here?
>
> >>> Wi is the observed response. a and b are the regression
> >>> coefficients. Zi follows a Beta distribution with parameters alpha and
> >>> beta (they are both unknown). Xi is a predictor variable, and Ei is
> >>> the error with mean zero and variance sigma^2.
> >> Does the linear model care how Z is distributed?
> >> That seems to be a separate question, if you care about it.
>
> >>> I need to estimate a, b, alpha, beta, and sigma^2 using method of
> >>> moments. I want formulas for them. I've never seen a linear model like
> >>> this before and have no idea how to tackle it. Any help or hints would
> >>> be greatly appreciated. Thanks in advance.
> >> --
> >> Rich Ulrich, wpi...@pitt.eduhttp://www.pitt.edu/~wpilib/index.html
>
> > Yes, it's a meaningful model. The Zi is there from a previous step,
> > which I haven't shown. The Zi follow a Beta distribution with unknown
> > parameters alpha and beta.
>
> But is Zi known?
>
> It's not clear to me if Zi is even identifiable. If it is, you might
> want to look at an EM algorithm, i.e. fit the standard linear model to
> Wi/Zi for estimated values of Zi, then estimate the Zi's, given a and b.
>
> Bob
>
> --
> Bob O'Hara
> Department of Mathematics and Statistics
> P.O. Box 68 (Gustaf H llstr min katu 2b)
> FIN-00014 University of Helsinki
> Finland
>
> Telephone: +358-9-191 51479
> Mobile: +358 50 599 0540
> Fax: +358-9-191 51400
> WWW:

http://www.RNI.Helsinki.FI/~boh/
> Blog:http://deepthoughtsandsilliness.blogspot.com/
> Journal of Negative Results - EEB:www.jnr-eeb.org- Hide quoted text -
>
> - Show quoted text -

Thanks. I'm a little confused though. Could you explain that further?
I'm looking for a formula for the estimators, and I don't know if the
EM algorithm will give that. Doesn't matter which method I use, as
long as I get some formulas.

== 1 of 4 ==
Date: Mon, Nov 19 2007 8:28 am
From: David Winsemius

Yves <sunder_1600@yahoo.com> wrote in
news:18472987.1195092592435.JavaMail.jakarta@nitrogen.mathforum.org:

> Thanks all, for your replies.
>
> What are some of the quicker and accurate methods?
>

Not sure about quicker, but more economical in the random number generation
steps, anyway. If you have two uniform "random" numbers in (0,1), U1 and
U2, you can get two "random" Normals, X1 abd X2, with this transformation:

X1=SQRT(-2*LN(U1))*COS(2*pi*U2)
X2=SQRT(-2*LN(U1))*SIN(2*pi*U2)

source: Johnson, Kotz, Balakrishnan. "Continuous Univariate Distributions,
vol 1"

The machine time will depend on the efficiency of the log and trig
functions.

--
David Winsemius

== 2 of 4 ==
Date: Mon, Nov 19 2007 11:27 am
From: hrubin@odds.stat.purdue.edu (Herman Rubin)

In article <fhfpu0$2o0q@odds.stat.purdue.edu>,
Herman Rubin <hrubin@odds.stat.purdue.edu> wrote:
>In article <13104907.1195060592975.JavaMail.jakarta@nitrogen.mathforum.org>,
>Yves <sunder_1600@yahoo.com> wrote:
>>Hi,

>>I read from Mark Joshi's Concept of Mathematical Finance pg 178 "..there is a simple method which gives reasonable, but not great, approximation is to simply add together 12 uniform variables and subtract 6. The results has correct mean, variance and third moment."

>>Could someone explain this idea? How can I find out about quick method?

>>Thanks.

>The idea is that the distribution is close to standard normal;
>it has the right mwan and variance, and the difference of the
>densities is "small".

>It is not quick; there are quicker and more accurate methods.

Rubin, Herman and Johnson, Brad (2006)
Efficient generation of exponential and normal deviates
Journal of Statistical Computation and Simulation, 76, 509-518
CISid: 261782

This gives one method of doing it, and also refers to
the Marsaglia inverse ziggurat method, which is another.
--
This address is for information only. I do not claim that these views
are those of the Statistics Department or of Purdue University.
Herman Rubin, Department of Statistics, Purdue University
hrubin@stat.purdue.edu Phone: (765)494-6054 FAX: (765)494-0558

== 3 of 4 ==
Date: Mon, Nov 19 2007 12:04 pm
From: "Luis A. Afonso"

*** Date: Nov 19, 2007 11:28 AM
Author: David Winsemius
Subject: Re: Turn a uniform number to normal random numbers
Yves
sunder_1600@yahoo.com
wrote in news:18472987.1195092592435.JavaMail.jakarta@nitrogen.mathforum.org: > Thanks all, for your replies. > What are some of the quicker and accurate methods? Not sure about quicker, but more economical in the random number generation steps, anyway. If you have two uniform "random" numbers in (0,1), U1 and U2, you can get two "random" Normals, X1 abd X2, with this transformation:
X1=SQRT(-2*LN(U1))*COS(2*pi*U2)
X2=SQRT(-2*LN(U1))*SIN(2*pi*U2)
source: Johnson, Kotz, Balakrishnan. "Continuous Univariate Distributions, vol 1"The machine time will depend on the efficiency of the log and trig functions. -
David Winsemius ****

MY RESPONSE

David
If you took attention to my
Nov 14, 2007 2:05 PM post
You should admit that I answered the OP in a more general form you posted.
Echoes are useless: isn't it?

*********

Luis Amaral Afonso

== 4 of 4 ==
Date: Mon, Nov 19 2007 6:00 pm
From: David Winsemius

"Luis A. Afonso" <licas_@hotmail.com> wrote in
news:16233415.1195502727932.JavaMail.jakarta@nitrogen.mathforum.org:

> *** Date: Nov 19, 2007 11:28 AM
> Author: David Winsemius
> Subject: Re: Turn a uniform number to normal random numbers
> MY RESPONSE
>
> David
> If you took attention to my
> Nov 14, 2007 2:05 PM post

Until now I had not. You and the rest of the audience are free to speculate
on why that might be so.

> You should admit that I answered the OP in a more general form you
> posted.
> Echoes are useless: isn't it?

I freely admit such, You should be the expert in useless posting, that is
for sure.

--
David

==============================================================================
TOPIC: hot sell multifarious sport shoes(www.nikewoo.com)
http://groups.google.com/group/sci.stat.math/browse_thread/thread/a7496b0d88b49efa?hl=en
==============================================================================

== 1 of 1 ==
Date: Mon, Nov 19 2007 10:31 am
From: mr cai

nikewoo@hotmail.com cjhuapt@yahoo.com.cn

www.nikewoo.com
Wholesale nike shoes,Adidas shoes,Bape Shoes,Clear jordan

shoes,Dolce&gabbana shoes,Dsquared2 shoes,Evisu shoes,Football
shoes,Gucci

shoes,Handbag,Hogan shoes,Hoodies and jacket,Ice cream

shoes,Jeans,Jersey,Kid shoes,Lacoste shoes,LV shoes,Nike AF1
shoes,Nike air

max shoes,Nike dunk shoes,Nike free shoes,Nike james shoes,Nike
jordan

shoes,Nike kobe shoes,Nike shox shoes,Other Nike,Prada Shoes,Puma

shoes,purse,Shirt(long sleeve),Timberland shoes,T-shirt,Winter
coat,Women

shoes,Women suit.

==============================================================================
TOPIC: Announcing DTREG 7 with Gene Expression Programming and Symbolic
Regression
http://groups.google.com/group/sci.stat.math/browse_thread/thread/186e987d8301748b?hl=en
==============================================================================

== 1 of 1 ==
Date: Mon, Nov 19 2007 10:34 am
From: "Phil Sherrod"

DTREG version 7 is now available with Gene Expression Programming and Symbolic
Regression.

DTREG home page -- http://www.dtreg.com

Gene Expression Programming is a procedure that mimics biological evolution to
create a computer program to model some phenomenon. The type of gene
expression programming implemented in DTREG is Symbolic Regression - so named
because it creates a symbolic mathematical or logical function.

Gene Expression Programming is an elegant and efficient approach to genetic
programming and symbolic regression. GEP was developed in 1999 by C ndida
Ferreira. Ferreira devised a system for encoding expressions that allows fast
application of a wide variety of mutation and cross-breeding techniques while
guaranteeing that the resulting expression will always be syntactically valid.
Experiments have shown that GEP is 100 to 60,000 times faster than older
genetic algorithms.

DTREG provides a full implementation of the Gene Expression Programming
algorithm developed by C ndida Ferreira. Here are some of the features of
DTREG's implementation:

* Continuous and categorical target variables
* Automatic handling of categorical predictor variables
* A large library of functions that you can select for inclusion in the model
* Mathematical and logical (AND, OR, NOT, etc.) function generation
* Choice of many fitness functions
* Both static linking functions and evolving homeotic genes
* Fixed and random constants
* Nonlinear regression to optimize constants
* Parsimony pressure to optimize the size of functions
* Automatic algebraic simplification of the combined function
* Several forms of validation including cross-validation and hold-out
* Computation of the relative importance of predictor variables
* Automatic generation of C or C++ source code for the functions
* Multi-CPU execution for multiple target categories and cross-validation

In ordinary mathematical regression, the procedure is given the form of the
function to be fitted to the data. This could be a linear function for linear
regression or a general mathematical function for nonlinear regression. The
regression procedure computes the optimal values of parameters for the function
to make the function fit a data set as well as possible, but the regression
procedure does not alter the form of the function.

With Symbolic Regression, the form of the function is not known in advance, and
it is the goal of the procedure to evolve a function that will fit the data.
In addition to evolving mathematical functions, symbolic regression can be used
to develop logic circuits using AND, OR, NOT, XOR and other logical functions.

In addition to Gene Expression Programming, DTREG provides these other types of
models:

* Support vector machine
* Multilayer perceptron
* RBF neural networks
* Decision trees
* Boosted decision trees
* Decision tree forests
* Probabilistic and General regression neural networks (PNN/GRNN)
* Cascade correlation networks
* Linear discriminant analysis
* Logistic regression

You can download a demo copy of DTREG from
http://www.dtreg.com/DownloadDemo.htm

Detailed information about Gene Expression Programming can be found at
http://www.dtreg.com/gep.htm

--
Phil Sherrod
(PhilSherrod 'at' comcast.net)
http://www.dtreg.com

(Decision trees, Neural networks and SVM modeling)
http://www.nlreg.com

(Nonlinear Regression)

== 1 of 2 ==
Date: Mon, Nov 19 2007 10:45 am
From: "Luis A. Afonso"

The only thing THAT MATTERS

***** Date: Nov 14, 2007 2:16 PM
Author: John Smith
Subject: Re: Turn a uniform number to normal random numbers

Luis,

You never bothered to finish discussing your last error before you committed another one. Please pay attention.

On Nov 11, 4:22 am
You wrote"From this values we obtain the CONFIDENCE INTERVALS of the two-tailed tests relative to the probabilities 99%, 98%, 95% the parameter be inside." Either the parameter is INSIDE or OUTSIDE. The probability that the parameter is inside is either 100% or 0%. Same for the probability that the parameter is outside. Please defend your assertion that there can be a 99% probability that a parameter is inside the interval. John
********************
My response

In this special matter (and others) Jonh Smith only sas nonsense. THE INSIDE-OUTSISE THEORY is STUPID in more than one aspect.
*** The explanation I repeat below
John Smith doe not LOOSE AN OPPORTUNITY to show us how IGNOEANT (and STUPID) is.*****

****

The only thing THAT MATTERS

. . Is simply that through the Monte Carlo (MC) simulative procedure we are able to attain Model Rigorous Sample Statistics (SS) and therefore Testing Hypotheses in a rigorous way.
From the SS we can obtain the respective Cumulative Probabilities (with a controlled maximum error provided by the Dvorestky - Kiefer - Wolfovitz) inequality, then the Critical Values at the conventional Significance Levels).
(Throughout DKW inequality I proved that the fundamental Lilliefors paper (On the Kolmogorov- Smirnov Test for Normality with Mean and Variance Unknown) the Critical Values are correct at 2 decimal places, at most).
The Readers have noted yet the Hypotheses Tests that matters (those without supposed known exact parameters) are WRONG because SS are only more or less approximated.
I don't know why Jack Tomsky, John Smith (and others) are fighting MC. It's clear crystal that the cited (gentlemen) has an alibi (?????????):

*** They never read the worlds known Conover's Practical Nonparametric Statistics where one can find how to get Cumulative Frequencies (Probabilities),
*** They seemly ignored the APPROXIMATIVE character the current Sample Statistics,
*** They admit never met the DKW inequality
HOWEVER
THEY CLAIM TO BE THE EXCLUSIVE Sci Sta Math REFEREES !!!!!!!!.
Absolutely ridiculous isn't it?
****

Luis Amaral Afonso
(Author of a Monte Carlo paper issued in a referee's controlled journal Revue de Statistique Appliquee, RSA).

== 2 of 2 ==
Date: Mon, Nov 19 2007 6:45 pm
From: John Smith

Nobody should believe anything Afonso writes until he answers these simple questions.

When a Monte Carlo created distribution is created, are the 1% and 99% percentiles statistics or parameters?

Bonus question: what is the role of the parameter in a Monte Carlo?

John

==============================================================================
TOPIC: Instructor's Manual Understanding Semiconductor Devices - Sima
Dimitrijev
http://groups.google.com/group/sci.stat.math/browse_thread/thread/dd861c3c164ca46d?hl=en
==============================================================================

== 1 of 1 ==
Date: Mon, Nov 19 2007 11:33 am
From: tiagao.plis@gmail.com

I m looking for a ebook for download: Instructor's Manual
Understanding Semiconductor Devices
by Sima Dimitrijev
plz helpm

== 1 of 5 ==
Date: Mon, Nov 19 2007 11:41 am
From: mprocopio@gmail.com

Hi folks,

I am doing some data analysis for my Ph.D. and have a fairly
straightforward statistical methods question.

I have five algorithms that I'm comparing, using three different
performance metrics. There are six datasets involved in the
comparison. You can visualize this as a table that is five rows in
height (each algorithm) and 18 rows in width (6 datasets, three
scoring metrics each).

All of my data are scale/continuous data, and are scores on the
interval [0, 1].

Let's consider just one dataset. I have a score (this is a mean socre,
so it also has std. dev) for each of the five algorithms, on three
scoring metrics (15 values total).

One experimental question I have is, how well to the metrics
correlate? Do they measure the same thing or different things? One way
to look at this question is to rank the five algorithms based on
score, so now I have three rank vectors, one for each metric.

Are you familiar with any methods that can give a well founded notion
of how well these ranks correlate?

The same question applies to comparing the five algorithms using just
ONE metric over SIX datasets; the formulation is simiar and/or
identical. The final research question would then draw conclusions
from the ranking from all six datasets using all three performance
metrics.

Thanks,

--Mike
Ph.D. Candidate, December 2007

== 2 of 5 ==
Date: Mon, Nov 19 2007 2:01 pm
From: Ray Koopman

On Nov 19, 11:41 am, mproco...@gmail.com wrote:
> [...]
> Let's consider just one dataset. I have a score (this is a mean socre,
> so it also has std. dev) for each of the five algorithms, on three
> scoring metrics (15 values total).
> [...]
Does the data from which you get a mean and s.d. for each of the 15
algorithm/metric combination also let you get the 15 x 15 matrix of
their variances and covariances? (The s.d.s are the square roots of
the diagonals of that matrix.)

== 3 of 5 ==
Date: Mon, Nov 19 2007 2:13 pm
From: mprocopio@gmail.com

On Nov 19, 3:01 pm, Ray Koopman <koop...@sfu.ca> wrote:
> On Nov 19, 11:41 am, mproco...@gmail.com wrote:> [...]
> > Let's consider just one dataset. I have a score (this is a mean socre,
> > so it also has std. dev) for each of the five algorithms, on three
> > scoring metrics (15 values total).
> > [...]
>
> Does the data from which you get a mean and s.d. for each of the 15
> algorithm/metric combination also let you get the 15 x 15 matrix of
> their variances and covariances? (The s.d.s are the square roots of
> the diagonals of that matrix.)

Each dataset consists of 100 "frames" against which the algorithm's
output is scored. So Algorithm A's performance on Dataset 1 is the
mean of its performance (Accoding to Metric M1) on each of the 100
frames in that dataset; the standard deviation is also reported from
this. The same is reported for Metrics M2 and M3, and then so on for
each of the six datasets.

== 4 of 5 ==
Date: Mon, Nov 19 2007 3:08 pm
From: Ray Koopman

On Nov 19, 2:13 pm, mproco...@gmail.com wrote:
> Each dataset consists of 100 "frames" against which the algorithm's
> output is scored. So Algorithm A's performance on Dataset 1 is the
> mean of its performance (Accoding to Metric M1) on each of the 100
> frames in that dataset; the standard deviation is also reported from
> this. The same is reported for Metrics M2 and M3, and then so on for
> each of the six datasets.

So does your basic data look like this, where each "x" is a score,
and values in the same row are matched in the sense that they all
came from the same frame in the same data set, but that the frames
are not matched from one data set to another (i.e., in analysis-of-
variance terminology, frames are nested within data sets, and
crossed with algorithms and metrics)?

Data Alg. A Alg. B Alg. C Alg. D Alg. E
Set Frame M1 M2 M3 M1 M2 M3 M1 M2 M3 M1 M2 M3 M1 M2 M3

1 1 x x x x x x x x x x x x x x x
1 2 x x x x x x x x x x x x x x x
1 : : : : : : : : : : : : : : : :
1 100 x x x x x x x x x x x x x x x

: : : : : : : : : : : : : : : : :

6 1 x x x x x x x x x x x x x x x
6 2 x x x x x x x x x x x x x x x
6 : : : : : : : : : : : : : : : :
6 100 x x x x x x x x x x x x x x x

== 5 of 5 ==
Date: Mon, Nov 19 2007 8:45 pm
From: mprocopio@gmail.com

Ray,

Thank you SO much for your assistance--I am grateful.

You have the general idea correct! Except in my rendering, ALG and
DataSet are transposed (Algs A-E are rows, Datasets are columns).
Further, I have condensed down the frames into a single value: the
mean and std. dev, so the nested rendering that you have provided is
thus compressed.

Dataset DS1 DS2 ... DS6

Metric M1 M2 M3 M1 M2 M3 ... M1 M2 M3
Alg

Alg. A x x x x x x x x x
Alg. B x x x x x x x x x
Alg. C x x x x x x x x x
Alg. D x x x x x x x x x
Alg. E x x x x x x x x x

The end goal is to come up with a final composite ranking of the
algorithms considering all of the data. This ranking of course answers
the key question, "Which algorithm is best?"

I have formulated my two research questions as follows:

1. Is the ranking of the algorithms dataset-dependent? (That is, given
some metric, does one algorithm perform best on all datasets, or does
the ranking of the algorithsm differ depending on the dataset?)

2. Is the ranking of the algorithms metric-dependent? (That is, given
a particular dataset, does one algorithm always perform best
regardless of the metric, or does the ranking of the algorithms differ
depending on the metric?)

Towards answering these questions, I checked out two books from the
library today:

Book 1 (1975)
Nonparametrics: statistical methods based on ranks E. L. Lehmann, with
the special assistance of H. J. M. D'Abrera

Book 2 (1999)
Nonparametric statistical methods 2nd Edition Myles Hollander, Douglas
A. Wolfe

However, I am having trouble adapting the methods here (specifically,
the elementary nonparametric ranking methods like the Wilcoxon Rank-
Sum method, etc.) to my particular research problem.

Any help or insight you could provide would be greatly appreciated.

Thank you,

--Mike

==============================================================================

You received this message because you are subscribed to the Google Groups "sci.stat.math"
group.

To post to this group, visit http://groups.google.com/group/sci.stat.math?hl=en

To unsubscribe from this group, send email to sci.stat.math-unsubscribe@googlegroups.com

To change the way you get mail from this group, visit:
http://groups.google.com/group/sci.stat.math/subscribe?hl=en

To report abuse, send email explaining the problem to abuse@googlegroups.com

==============================================================================
Google Groups: http://groups.google.com?hl=en

Senin, 19 November 2007

16 new messages in 14 topics - digest

sci.stat.math
http://groups.google.com/group/sci.stat.math?hl=en

sci.stat.math@googlegroups.com

Today's topics:

==============================================================================
TOPIC: Anova with continuous variable
http://groups.google.com/group/sci.stat.math/browse_thread/thread/d44fa29a8fefa03a?hl=en
==============================================================================

== 1 of 1 ==
Date: Sun, Nov 18 2007 12:35 am
From: Emmanuel Charpentier

Bruce Weaver a écrit :

[ Snip... ]

>> You're barking up the wrong tree : what you're tring to do is not but
>> regression.
>
> I think you mean it is "nothing but regression", perhaps?

No : I meant "... not ANOVA but regression". Typo...

>>
>> Indeed, both are special cases of general linear models. I suggest to
>> look that up in a good intermediate-level textbook.
>>
>> I wouldn't use the general-purpose Matlab for this kind of problems, but
>> a stattistical package such as R.
>>
>> Furthermore, there are some general (psychophysiological) reasons to
>> question the linearity of the relationships you're trying to discover. A
>> nonlinear regression might be more appropriate...
>>
>> HTH
>>
>> Emmanuel Charpentier
>
>
> Emmanuel, if you are referring to what has sometimes been called
> "polynomial" regression (e.g., inclusion of both X and X^2 to model a
> quadratic relationship between X and Y), that is still linear regression
> in the terminology I am familiar with. I.e., it is linear in the
> coefficients, and uses the same equations as when the functional
> relationship is linear.

Nope. I was thinking about the fact that stimulus/response curve are
generally not linear. In the "useful" or "physiologic" range, they
appear to be logarithmic, but with thresholds effects, both for low
stimuli (sensitivy threshold) and high stimuli (receptor saturation),
which might be better modeled by sigmoïds, for example. And this is
*not* well modeled by polynoms (if you don't believe me, take the Taylor
expansion of f(x)=e^x/(1+e^x) around x=0, for example...).

But in real psychophysiology, things can be much more intricate... I'd
rather try to read the literature relevant to taste perception, build a
couple of "reasonable" models and try to assess them via non-linear
least squares and such...

Emmanuel Charpentier

==============================================================================
TOPIC: Help - Correlation of measurable and ordinal data plssss
http://groups.google.com/group/sci.stat.math/browse_thread/thread/aa9fda68325d8e7d?hl=en
==============================================================================

== 1 of 1 ==
Date: Sun, Nov 18 2007 12:58 am
From: Ray Koopman

On Nov 16, 7:25 am, "geetha.sh...@gmail.com" <geetha.sh...@gmail.com>
wrote:
> Hi
> I am need to design an experiment for my research work, which involves
> ranking of a product finish(plastic panels).
>
> Each of the Panels has 6 measurable variables associated (say
> thickness, shininess, color, gloss, etc., which can be MEASURED by an
> instrument) In a group of 100 panels I have selected only 18 panels
> which have low, medium and high value of each Variable, but all other
> variable have average value of their own [i.e., low,medium, high value
> of A1 and Average values of A2,A3,A4,A5,A6(100 panels)]
> ---------------------------------------------------------
> Ex:
> Panel No. A1 A2 A3 A4 A5 A6
>
> Panel 1: 2 avgA2 avgA3 avgA4 avgA5 avgA6
> Panel 2: 14 avgA2 avgA3 avgA4 avgA5 avgA6
> Panel 3: 27 avgA2 avgA3 avgA4 avgA5 avgA6
> Panel 4: avgA1 5 avgA3 avgA4 avgA5 avgA6
> Panel 5: avgA1 17 avgA3 avgA4 avgA5 avgA6
> Panel 6: avgA1 36 avgA3 avgA4 avgA5 avgA6
> .
> .
> .
> Panel 18:avgA1 avgA2 avgA3 avgA4 avgA5 35
> ----------------------------------------------------
>
> The above 18 panels are evaluated i.e, just RANKED by people based on
> what they like (Ranking based on the overall Finish ONLY, so they are
> not aware of the SIX variables)
>
> The people who are ranking the plastic panel are focusing on the
> Product FINISH only. They Rank from 1 to 18 ( 1 = BEST and 18 =
> WORST )
> Person Panel 1 Panel 2 Panel 3 ...... Panel 18
> 1. 4 14 7 1
> 2. 13 10 3 5
> 3. 9 7 15 11
> 4.
> 5.
> .
> .
> .
> 99.
> 100.
>
> My aim is to determine which among the six variable is more
> influential/ related to RANK and develop a STANDARD for this
> instrument.
>
> My problem is how can I do statistical analysis to develop an STANDARD
> if I have a RANK DATA and a MEASURABLE DATA ...
>
> The Rank data does not follow a normal distribution even after
> transformation.
>
> Of the 6 variables only 3 variables are normally distributed and the
> rest 3 are not Normally distributed.
>
> Is it necessary to have each of the data ( rank, A1, A2, A3, A4, A5 ,
> A6) to be normally distributed to do regression analysis.
>
> Can you please suggest me some solution for how approach this very
> Tricky problem.

First, there is no one right way to analyse your data. No matter what
you do, there will be places where people can say "yes, but...."

I would treat the average of the 100 rankings as an interval variable
and look its scatterplot with each of the 6 measured variables. If any
plot is obviously nonlinear, then transform the variable to linearize
the relation. If your job is to pick the one best variable, then pick
the one with the highest correlation. I don't know what you mean when
you say that you are to develop a standard for this instrument.

There are many other potentially interesting analyses that could be
done. For starters, look at the standard deviation of the ranks for
each panel. A large SD indicates lack of agreement among the raters.
You may want to incorporate this information into the correlation by
doing a weighted correlation -- weight = 1/variance -- that pays less
attention to the panels about which the raters disagreed more.

People often suggest Kendall's coefficient of concordance for such
sets of rankings, so do it, even though it tends to be noninformative,
because someone may ask about it. However, you'll get more information
from doing a component analysis of the rankings. That will tell you
how many different points of view there were about which panels were
better. If there is not one point of view that is clearly dominant
then looking at the average rankings may not be the best approach.
The correlations of the "points of view" factors from the CA with
the (possibly transformed) measured variables might be enlightening.

==============================================================================
TOPIC: MI5 Persecution: Barbican Library 6/2/2003 (29357)
http://groups.google.com/group/sci.stat.math/browse_thread/thread/b39663f066aefd9c?hl=en
==============================================================================

== 1 of 1 ==
Date: Sat, Nov 17 2007 9:13 pm
From: MI5Victim@mi5.gov.uk

Barbican Library - 6/Feb/2003

Certainty level: 100%

Here's an item from early February 2003, at which time Security Service were re-igniting hostilities against me, a direct result of my appeal
for a legal hearing against their organisation. On Thursday 6/Feb/2003 I went to the Barbican in central London with my mother to visit the library
and have a meal. Security Service naturally knew about it because it had been mentioned the previous day in our house which is under constant
surveillance on grounds of "National Security", so they may have been able to inform staff there to say something to me, as has been done elsewhere.
Two of the people at the Enquiries desk in the library obviously knew of my circumstances, I spoke to one of them who was very polite, and while
I was waiting I heard the other speaking with a little less politeness, as recorded on this audio file. He said, "I spy", to start a game of
guess what "I spy"; unsubtle, MI5 Spy. Then, at the 34 second mark on this clip he said;

"So obvious you couldn't even get that. I shall have to kill you. ha, ha, ha"

The words "I shall have to kill you" are interesting because such overt expression is rarely heard. Security Service wish me extinct.
Some years ago they were openly shouting "suicide" at me, so that is established. But there is an element of doublethink in their attitude;
they use people's natural prejudice against mentally ill, who are popularly painted by media as being aggressive, to try to tinge the group's
view of me and MI5's actions against me. The Security Service religion is that the country's enemies are deadly, and must therefore be
extinguished. Hence, we must seize eagerly upon the disabled schizophrenic from Clapham, and throttle him, because who knows what'll happen if we don't.

There may be a subtle agenda of population control in what the Security Service have been doing in this country since 1990. In the current issue
of 2600 Magazine (the Hacker quarterly, which I read from time to time) there is a quote from Hermann Goering on page 2; "the people can always
be brought to the bidding of the leaders. All you have to do is tell them they are being attacked. It works the same in any country".
2600 printed the quote as a comment on the projected war against Iraq, which does not appear to pose any threat to the territory of
those countries which wish to invade it. However the quote is somewhat relevant to my case, which is widely known about within Britain;
the security service instructs all right thinking citizens to do MI5's bidding and band together against a common enemy, while at the same time
there is a suggestion of "look what'll happen to you if you don't obey us".

29357

--
Posted via NewsDemon.com - Premium Uncensored Newsgroup Service
------->>>>>>http://www.NewsDemon.com<<<<<<------
Unlimited Access, Anonymous Accounts, Uncensored Broadband Access

==============================================================================
TOPIC: Interesting (but difficult) question - calculating 'implied'
probabilities of a wager
http://groups.google.com/group/sci.stat.math/browse_thread/thread/245aac10e44fcef8?hl=en
==============================================================================

== 1 of 1 ==
Date: Sun, Nov 18 2007 2:51 am
From: Anonymous

Pavel314 wrote:
> "Anonymous" <no.reply@here.com> wrote in message
> news:EZqdnXCpq9-olaDanZ2dnUVZ8sHinZ2d@bt.com...
>
>>
>>Pavel314 wrote:
>>
>>
>>>"Pavel314" <Pavel314@NOSPAM.comcast.net> wrote in message
>>>news:SoCdnbzFjODOpaHanZ2dnUVZ_sOrnZ2d@comcast.com...
>>>
>>>
>>>>"Anonymous" <no.reply@here.com> wrote in message
>>>>news:Ou6dnXpmPox49KTanZ2dnUVZ8s-qnZ2d@bt.com...
>>>>
>>>>
>>>>>Here is a hypotheical scenario.
>>>>>
>>>>>A friend and I decide to visit the local county fair. There is a
>>>>>competition to see who can throw a heavy ball the highest. I bet my
>>>>>friend that I can throw the heavy metal ball more than X metres high.
>>>>>
>>>>>He in turn, says "I'll pay you a dollar for every Y centimeters that you
>>>>>can throw the ball above X meters - BUT to make it worth my while, you
>>>>>have to PAY ME Z dollars for me to take on the bet".
>>>>>
>>>>
>>>>>From the above, my friend has calculated (implicitly from the wager he
>>>>
>>>>>has made), the probability of me being able to throw the ball above X
>>>>>metres. How may I calculate the probaility, so I can work out the
>>>>>(implied) odds of my success?
>>>>>
>>>>>What methodology/logic/technique may I use to calculate the probability
>>>>>of me throwing the ball above X metres (based on the wager given above)?
>>
>>Hi Paul, thanks for your feedback. Incidentally, I agree with you - I am
>>thinking along the same lines - it may not be possible to calculate the
>>oddds, without knowing the distribution of the height of the throws. I'll
>>do some more thinking ...
>
>
>
> After another lunch hour spent considering the situation, here's what
> I've come up with. I still don't have an answer but maybe this will help.
>
> Assume your throw distance to be normally distributed, not an unnatural
> assumption.
>
> YOUR BET
>
> You estimate that your average throw, trying to make the longest throw
> you can, will be 100 * X + E centimeters, where X meters is the minimum
> distance you have to throw to win the bet. You also estimate that the
> limit point, X meters, is two standard deviations below your mean throw;

Thats probably too generous (i.e. not likely to happen in the real
world), but for the purposes of illustrating the underlying 'mechanics'
of the problem - ok.

> this gives you only a 2.5% chance of losing the bet. So the standard
> deviation of your throw is E / 2.

Erm, I don't follow, why is the stddev E/2 ?. BTW, you haven't yet
defined E, I was assuming that it was the error term (i.e. a SNV ~ N(0,1))

>
> FRIEND'S BET
>
> Assume that our friend has seen you throw in the past and makes the
> same estimates that you have on mean and standard deviation. Wanting the
> same risk that you put on your bet, he sets the parameters of his bet so
> that he doesn't have to pay you unless your throw goes more than two
> standard deviations above the estimated mean, 100 * X + 2 * E.
>
> He will pay you one dollar for each Y centimeters you throw past 100 *
> X. He will have already collected Z dollars for taking the bet, so to
> break even if you throw to two standard deviations beyond the mean we
> need:
>
> Z = (2 * E) / Y
>
> Solving for E,
>
> E = (Z * Y) / 2
>
> This puts your mean throw as
>
> ( 100 * X ) + ((Z * Y) / 2)
>
> and the standard deviation of your throw as
>
> (Z * Y) / 4
>
> So at least we have the distribution of your throw in terms of your friend's
> bet parameters.
>
> I'm sure there's a way to calculate payback by the probability of each Y
> segment but I don't have that yet.
>
>
> Paul
>
>
>
>

== 1 of 3 ==
Date: Sun, Nov 18 2007 3:46 am
From: Claire Blair

I have a basic question, which I hope someone can answer for me.

I have sample data that consists of binary (Yes/No) data. The Yes's
correspond to a 'success' event, and the No's correspond to a 'Failure'
event.

I want to know how I can use the data to model and predict the
probability of a success event.

I think the logistic model is what I should use, but I am not sure.

Please advise.

== 2 of 3 ==
Date: Sun, Nov 18 2007 5:28 am
From: "Nasser Abbasi"

"Claire Blair" <no.reply@here.com> wrote in message
news:GZCdnScOP_M5u93anZ2dnUVZ8qWhnZ2d@bt.com...
>I have a basic question, which I hope someone can answer for me.
>
> I have sample data that consists of binary (Yes/No) data. The Yes's
> correspond to a 'success' event, and the No's correspond to a 'Failure'
> event.
>
> I want to know how I can use the data to model and predict the probability
> of a success event.
>
> I think the logistic model is what I should use, but I am not sure.
>
> Please advise.

I think what you want is to fit a probability distribution to your data?

Assuming your X's (events) are i.i.d. you could try to fit Binomial or may
be Poisson? (but with Poisson, we need n to be large and p very small to get
good approximation to the binomial).

So, For Binomial, using Maximum likelihood, p comes out to be X_bar, i.e.
the probability which maximizes the likelihood of observing your data is
this probability. (X_Bar is the mean of the sample data). For Poisson,
also using Max. likelihood, p is X_bar.

Now you can use your 'model' distribution with the above parameter to
calculate other probabilities.

Nasser

== 3 of 3 ==
Date: Sun, Nov 18 2007 9:08 am
From: richardstartz@comcast.net

On Sun, 18 Nov 2007 11:46:40 +0000, Claire Blair <no.reply@here.com>
wrote:

>I have a basic question, which I hope someone can answer for me.
>
>I have sample data that consists of binary (Yes/No) data. The Yes's
>correspond to a 'success' event, and the No's correspond to a 'Failure'
>event.
>
>I want to know how I can use the data to model and predict the
>probability of a success event.
>
>I think the logistic model is what I should use, but I am not sure.
>
>Please advise.

Assuming that by "model" you mean you have explanatory variables that
you think help predict success or failure, then yes, a logistic model
(or a probit) is appropriate.
-Dick Startz

==============================================================================
TOPIC: MI5 Persecution: Bernard Levin - The Times (31532)
http://groups.google.com/group/sci.stat.math/browse_thread/thread/6ff0b4bba20f5861?hl=en
==============================================================================

== 1 of 1 ==
Date: Sun, Nov 18 2007 12:20 am
From: MI5Victim@mi5.gov.uk

Fanatic's Fare for the Common Man

Certainty level: 90%

The article reproduced below was penned by Bernard Levin
for the Features section of the Times on 21 September 1991. To my mind, it
described the situation at the time and in particular a recent meeting with
a friend, during which I for the first time admitted to someone other than
my GP that I had been subjected to a conspiracy of harassment over the
previous year and a half.

There is a madman running loose about London, called David Campbell; I have
no reason to believe that he is violent, but he should certainly be
approached with caution. You may know him by the curious glitter in his
eyes and a persistent trembling of his hands; if that does not suffice, you
will find him attempting to thrust no fewer than 48 books into your arms,
all hardbacks, with a promise that, if you should return to the same
meeting-place next year, he will heave another 80 at you.

If, by now, the police have arrived and are keeping a close watch on him,
you may feel sufficiently emboldened to examine the books. The jackets are
a model of uncluttered typography, elegantly and simply laid out; there is
an unobtrusive colophon of a rising sun, probably not picked at random.
Gaining confidence - the lunatic is smiling by now, and the policemen, who
know about such things, have significantly removed their helmets - you
could do worse than take the jacket off the first book in the pile. The
only word possible to describe the binding is sumptuous; real cloth in a
glorious shade of dark green, with the title and author in black and gold
on the spine.

Look at it more closely; your eyes do not deceive you - it truly does have
real top-bands and tail-bands, in yellow, and, for good measure, a silk
marker ribbon in a lighter green. The paper is cream-wove and acid-free,
and the book is sewn, not glued.

Throughout the encounter, I should have mentioned, our loony has been
chattering away, although what he is trying to say is almost impossible to
understand; after a time, however, he becomes sufficiently coherent to make
clear that he is trying to sell the books to you. Well, now, such quality
in bookmaking today can only be for collectors' limited editions at a
fearsome price - £30, £40, £50?

No, no, he says, the glitter more powerful than ever and the trembling of
his hands rapidly spreading throughout his entire body; no, no - the books
are priced variously at £7, £8 or £9, with the top price £12.

At this, the policemen understandably put their helmets back on; one of
them draws his truncheon and the other can be heard summoning
reinforcements on his walkie-talkie. The madman bursts into tears, and
swears it is all true.

And it is.

David Campbell has acquired the entire rights to the whole of the
Everyman's Library, which died a lingering and shameful death a decade or
so ago, and he proposes to start it all over again - 48 volumes this
September and 80 more next year, in editions I have described, at the
prices specified. He proposes to launch his amazing venture simultaneously
in Britain and the United States, with the massive firepower of Random
Century at his back in this country, and the dashing cavalry of Knopf
across the water, and no one who loves literature and courage will forbear
to cheer.

At the time this article was written I had believed for some time that
columnists in the Times and other journalists had been making references to
my situation. Nothing unusual about this you may think, plenty of people
have the same sort of ideas and obviously the papers aren't writing about
them, so why should my beliefs not be as false as those of others?

What makes this article so extraordinary is that three or four days
immediately preceding its publication, I had a meeting with a friend,
during the course of which we discussed the media persecution, and in
particular that by Times columnists. It seemed to me, reading the article
by Levin in Saturday's paper, that he was describing in some detail his
"artist's impression" of that meeting. Most telling are the final
sentences, when he writes, "The madman bursts into tears, and swears it is
all true. And it is." Although I did not "burst into tears" (he seems to be
using a bit of poetic licence and exaggerating) I did try hard to convince
my friend that it was all true; and I am able to concur with Mr Levin,
because, of course, it is.

At the beginning of the piece Levin reveals a fear of being attacked by the
"irrational" subject of his story, saying "I have no reason to believe that
he is violent, but he should certainly be approached with caution". This
goes back to the xenophobic propaganda of "defence" against a "threat"
which was seen at the very beginning of the harassment. The impression of a
"madman running loose" who needs to be controlled through an agency which
assigns to itself the mantle of the "police" is also one which had been
expressed elsewhere.

In the final paragraph of this extract, his reference to Everyman's Library
as having "died a lingering and shameful death a decade or so ago" shows
clearly what sort of conclusion they wish to their campaign. They want a
permanent solution, and as they are prevented from achieving that solution
directly, they waste significant resources on methods which have been
repeatedly shown to be ineffective for such a purpose.

31532

--
Posted via NewsDemon.com - Premium Uncensored Newsgroup Service
------->>>>>>http://www.NewsDemon.com<<<<<<------
Unlimited Access, Anonymous Accounts, Uncensored Broadband Access

==============================================================================
TOPIC: MI5 Persecution: Come back, Norma! (33707)
http://groups.google.com/group/sci.stat.math/browse_thread/thread/32bbca15e7aa240d?hl=en
==============================================================================

== 1 of 1 ==
Date: Sun, Nov 18 2007 1:53 am
From: MI5Victim@mi5.gov.uk

Private Eye, 23 Oct 1992

Certainty level: 25%

I'm not really sure whether this cover was intended to get at me,
or whether it was re-interpreted after publication to be about me.
This issue of Private Eye came out in October 1992, by which time I had
been under severe continuous pressure at work and from the general
population in Oxford for many months. It had John Major saying to his
wife, "Come back, Norma!" under the title "Major's support lowest ever".

There's a story behind this. In late October I was in the local pub
(the Rose and Crown, nice traditional name eh) with two people from
work, Simon and Phil. Phil had with him a copy of the current Private
Eye. These are both "nice people" and on my side, I hasten to add.
Simon pointed out the message on the magazine's cover to Phil, and asked
"what do you think about that". Phil answered, "Well usually they
[Private Eye] get it right". This exchange happened in front of me.
Although I was ill at the time (this was before I'd started taking
medicines) I hadn't read anything into the Eye cover until these guys
pointed it out to me.

Sometime later, again in the same road, a student shouted to
one of his friends, "so when are you COMING BACK?", again in front
of me.

What I realised Phil thought it meant was a double-entendre, the
"coming" referring to the act of ejaculation, the "back" referring
to the human back-side. So in a play on words you get a person who
is referred to as a back-side ejaculating. Charming.

Of course, Phil could have been wrong. Perhaps there was no such meaning
intended by Private Eye. Perhaps he saw meaning which wasn't there.
Perhaps the moon is made from green cheese. Who knows?

33707

--
Posted via NewsDemon.com - Premium Uncensored Newsgroup Service
------->>>>>>http://www.NewsDemon.com<<<<<<------
Unlimited Access, Anonymous Accounts, Uncensored Broadband Access

==============================================================================
TOPIC: MI5 Persecution: Victor Lewis-Smith (35882)
http://groups.google.com/group/sci.stat.math/browse_thread/thread/40806c693348db04?hl=en
==============================================================================

== 1 of 1 ==
Date: Sun, Nov 18 2007 2:51 am
From: MI5Victim@mi5.gov.uk

Victor Lewis-Smith

Certainty level: 10%

Victor Lewis-Smith has issued a denial through his webmaster
John Hayward-Warburton regarding this article; he says quite categorically
it's not written about me. I kind-of sort-of believe him, but I think
the conspiracy is so wide that the phrase "Eventually, I had to leave
because they were all trying to kill me" could have been intended to be
as if from any paranoid person, but could have been influenced by my
case in the particular. So the influence could have been sort-of
subconscious.

This article is from the Evening Standard of Thursday May 8, 1997.
It is titled "Goon, and best forgotten" and may be found on page 31.

I think VLS has written/produced stuff specifically about me in the past,
both in his Evening Standard column, one of which is reproduced below
(in another case, he was talking about computer hacking, and I think that
was directed at me), and a television programme in around late 1993
when he has a taxi driver who says "they're all trying to kill me guv"
and VLS responds "you're supposed to be quite intelligent aren't you".

However, his webmaster has issued a formal denial on his behalf, so it
is not for me to oppose what he has stated categorically.

35882

--
Posted via NewsDemon.com - Premium Uncensored Newsgroup Service
------->>>>>>http://www.NewsDemon.com<<<<<<------
Unlimited Access, Anonymous Accounts, Uncensored Broadband Access

== 1 of 1 ==
Date: Sun, Nov 18 2007 7:13 am
From: lior

i have a problem to find the cofficient curve by using MATLAB when there is no short sale

(i cant use excel because my data is so big that i cant find the inverse matrix)

thanks a lot

==============================================================================
TOPIC: MI5 Persecution: Introduction to Sent Faxes (1082)
http://groups.google.com/group/sci.stat.math/browse_thread/thread/cf5a54a9193375bc?hl=en
==============================================================================

== 1 of 1 ==
Date: Sun, Nov 18 2007 4:43 am
From: MI5Victim@mi5.gov.uk

Introduction to the Sent Faxes webpages

Between 1998 and April/2000 I sent some thirty articles on the subject of the MI5 Persecution to
British politicians, media, legal and diplomatic entities in the UK. Of those articles 24 are reproduced
on this part of the website. They were originally composed as Microsoft Word documents, and saved to HTML
form for publication here. A few of them were slightly edited in format for readability.

You will see that these articles cover quite comprehensively what has been going on since 1990, there being
quite a lot of material to cover, since it is a rare week that goes by without MI5 making some harassing
or threatening action against me. "How could it be True?"... they use the mental illness which they induced
in me as a ready explanation for my complaints. Who started it, why did it start, why it continues...
these faxed articles provide examination in more depth of the treatment in the Frequently Asked Questions webpages.
The sheer meaninglessness of the Secret Police actions is highlighted; their operatives make their livelihoods
by cynically exploiting the taxpayers, when in a more responsible country they would be looking at the inside of
prison cells.

The validity of this observation is proven by their reaction to my several suggestions in the articles that I
would cease sending faxes if MI5's agents (and it's always the same people doing the persecution) desisted from
continually harassing me. A number of times, you will see in the faxed articles, I have suggested that
we come to a "peace of mind agreement" where I cease complaining and they cease harassing. On one occasion,
I wrote to Hugh S.-W., the director of OCTS where I was employed in 1992-94 and who is aware of the
circumstances, asking that he intercede to bring this matter to a close, by initiating a meeting between
myself and the persecutors to mutually settle our differences; he didn't reply, but MI5 did
soon after - they said "This is what you wanted, wasn't it?, some sort of confrontation?".
The reality is the people stalking me are not interested in any settlement; for them crime pays, and
pays handsomely; moreover it is State-approved crime; the Police will never investigate or take action;
it is corruption on an incredible level and scale, and it has gone on for over a decade.

You will see that the articles contain three elements; firstly, there are original written articles,
many in the first half of 1999 when I was writing new articles every week and sending them by email and
fax during the weekend; then there are "MI5 Persecution Update" articles, which list happenings recent to
a particular transmission; and finally there is the "old faithful" Frequently Asked Questions article
which was sent on a few occasions during the three year period. These articles generated a number of
interesting responses from politicians and other recipients, many of which are documented in the
"received faxes" area of these webpages.

I hope you enjoy reading these articles. I'm not as angry as these articles necessarily make out,
only sometimes, usually soon after the persecutors have said something unpleasant at me; the purpose
of putting lots of fire and ire into the mix is to try to chuck it into the public domain which might
lead to MI5 being forced to curb their actions. Unfortunately that aim was not achieved, despite
many, many thousands of faxes being sent. Also, if any policemen are reading this, then I'm (a bit) sorry
about the rude things I said about "the Bill" in one or two of the articles; the police have never done me
any harm (yet) by commission, only by omission of doing anything to investigate my complaints or treat
them seriously.

1082

--
Posted via NewsDemon.com - Premium Uncensored Newsgroup Service
------->>>>>>http://www.NewsDemon.com<<<<<<------
Unlimited Access, Anonymous Accounts, Uncensored Broadband Access

==============================================================================
TOPIC: MI5 Persecution: Faxes Sent to Diplomatic/Legal (3257)
http://groups.google.com/group/sci.stat.math/browse_thread/thread/fe23ce7135ca2458?hl=en
==============================================================================

== 1 of 1 ==
Date: Sun, Nov 18 2007 5:55 am
From: MI5Victim@mi5.gov.uk

Faxes Sent to British Diplomatic/Legal logs

During the 1998-2000 period my records indicate that I sent at least 3,642 faxes to diplomatic and legal offices
located in the British isles, of which 3,345 went via TPC's email-to-fax service and 297 were sent via fax-modem
direct from my computer. The actual figure is somewhat higher since, for most of this period, whenever a recipient
asked to be deleted from my mailing list, I totally wiped all entries including that from the logfile.

In the records "Y" indicates successful transmission from fax-modem, "N" indicates failed transmission from fax-modem.
"E" indicates an error occurred while transmitting via fax-modem and a fax may have been only partly transmitted.
"y" and "n" indicate success or failure via TPC.

In April 2000 I changed the method of operation by recording "R" when a recipient requested removal, rather than
wiping their details from my mailing list and records; and keeping "B" records for when TPC actioned a ban on a
recipient's fax-number, but the recipient did not write to ask me to cease faxing. The purpose of these changes
was to keep more accurate and complete records, but the intention was thwarted by the police complaint.

441712011004 yyyyYyynyyyyyny
441712210448 yyynYnynyyyyy
441712212818 yyyyYyyyyyyyy
441712215685 yyyyYyyyyyyyy
441712250947 nynnYyynyyyyy
441712252130 nnnnYynnyyyyn
441712253024 ynyyYyyyyyyyy
441712253862 yyyyEyyyyyyny
441712275503 yyyyYyyyyyyyy
441712293215 yyyyYyyyyyyyy
441712330174 nynyYnnnnnnnn
441712331612 yyyyYyyyyyyyy
441712343222 yyynYyyyyyyyy
441712351286 nyyyYnyyyyyyy
441712352263 yyyyYyyynyyyy
441712353680 yynnYyynyyyyy
441712354463 yyyyYyyyyyyyy
441712354557 yynnYnynyyyyy
441712354621 yyyyYyyynyyyy
441712355161 ynnnYyyyyyyyy
441712355684 yyyyEyyyyyyyy
441712359048 yynyYnyynnnnn
441712359717 nyyyYyyyyyyyn
441712359734 nnyyNnynynyny
441712359905 nyyyYyyyyyyyn
441712405333 yyyyYyyyyyyyy
441712407722 yyyyYyyyyyyyy
441712421447 yyyyYyyyyyyyy
441712422511 yyyyYyyyyyyyy
441712424221 yyyyYyyyyynny
441712424282 yyyyYyyyyyyyy
441712425434 yyyyYynnyyyyy
441712427803 yyyyYyyyyyyyy
441712428502 yynyYyynynnny
441712431699 yyyyYyyyynyyy
441712451287 yyyyEyyyyyyyn
441712456583 yyyyYyyyyyyyy
441712456961 nyyyYnynyyyyy
441712456993 ynnnYynnynnnn
441712459552 yyyyYyyyyyyyy
441712485735 yyyyYyyynyyyy
441712553760 yyyyYyyyynyyy
441712569992 yyyyYyyynnnnn
441712586333 yyyyYyyyyyyyy
441712595392 yyyyYynynyyyy
441712596213 yyyyYyynyyyyy
441712596487 ynnnYyyynnnyn
441712627970 yynnYnnnynnnn
441713060095 yyyyYyyyyyyyy
441713181301 yyyyYynyyyyny
441713181349 yyyyYyyyyyyyy
441713236717 nnnnYnnnyyyyy
441713321493 nnnnYyynyyyny
441713330270 yyyyYyyyyyyyy
441713338831 yynnYyynnnnnn
441713340242 yyynYnyyyyyy
441713440292 yyyyYyyyyyyyy
441713530075 yyyyYyyyyyyyy
441713530329 yyyyYyyyyyyyy
441713530339 yynnYyynynnnn
441713530425 yyyyYyyyynyy
441713530464 yyyyYyynnynny
441713530652 yyyyYyyyyyyyy
441713530659 yyyyYyyyyyyyy
441713530667 yyyyYyyyyyyyy
441713530937 yyyyYyyynnnnn
441713530998 yyyyYyyyyyyyy
441713531261 yyyyYyyynyyyy
441713531344 yynnYyyyyyyy
441713531488 yyyyYyyyyyyyy
441713531699 yyyyYyyynyyyy
441713531724 yyyyYyyyyyyyy
441713531726 yynyYyyyyyyyy
441713531794 yynnNnnnnnnnn
441713532144 yyyyYyynyyyyn
441713532221 yyyyYyyyyyyyy
441713532647 yyyyYynnnnnnn
441713532911 yyyyEyyyyyyyy
441713533319 yyyyYyynynnnn
441713533383 yynnNyynynnnn
441713533929 yyyyYyyynnnnn
441713533978 yyyyYyyyyyyy
441713534170 yyyyNyyyyyyy
441713534410 yynYyynynnnn
441713534637 yyyyYyyyynyyy
441713534979 yyyyYyyyynnyy
441713535319 yyyyYyyyyyyyy
441713535422 ynyYyyyyyyyy
441713535778 yyyYyyyyyyyy
441713536271 yyyyYyyyyyyyy
441713537622 nynyYyyyyyyyy
441713537741 yyyyYnyynnnnn
441713537772 yynnYyynynnn
441713537943 yyyyYyyyyyyyy
441713538188 yyyyYyyyyyyyy
441713538339 yyyyYyyyyyyyy
441713538791 yyyyYyyyyyyyy
441713538815 nyyyNnnnnnnnn
441713539292 yyyyYyyyyyyyy
441713539439 yyyyYyyyyyyyy
441713539924 yyyyYyyyyyyyy
441713539949 yyyyYyyyyyyyy
441713541166 yyyyYyyynnnyy
441713553568 yyyyYyyyyyynn
441713610005 yyyyYnnnyyyyy
441713703838 yyyyYyyyyyyny
441713707773 yyyyYyyyyynyy
441713711222 nnnnNnnnnnnnnn
441713738743 yyyyYyyyyyyyyy
441713780712 nynyYyynnnnnnn
441713795634 nnynNynnnnnnnn
441713870310 yyyyNyyyyynnyn
441713873289 yyyyYyyyyynyyy
441714040445 yyyyYyyyyyyyyy
441714041405 yyyyYyyyyyyny
441714042283 yyyyYyyyyyyyyy
441714043139 yyyyYnyyyyyyy
441714043900 yyynYyyyynyyyy
441714044812 yyyyYyyyyyynyy
441714045032 yyyyYyyyyyyyyy
441714046659 yyyyYyyynnnnnnnn
441714048089 yyyyNyyynnnnnnnn
441714050001 yyyyYyyyyyyyyyy
441714050798 yynnYyynynnnnnn
441714051166 yyyyYyyyyyyyyny
441714051360 yyyyYyyyyyyyyyy
441714051387 yyyyYyyyyyyyyyy
441714052084 ynnnYyyyyyyyyyyy
441714053082 ynnnYyynyyynnn
441714053896 yyyyYyyyyyyyyyn
441714054078 yyyyYyyyyyyyyy
441714054267 yyyyYyyyyyynyy
441714054934 yyyyYyyyyyyyB
441714056680 yyyyYyyyyyyyyyy
441714057028 yyyyEyyyyyyyyy
441714057456 yyyyYyyynyyyyyy
441714059955 yyyyYyyyyyyyyyy
441714130374 yyyyYnyyyyyyyyy
441714300502 yyyyYyyynnnnnnn
441714301522 yyyyYyyyyyyyyy
441714301677 yynnYyynynnnnnn
441714309171 yyyyYnnyyyyyynn
441714360319 yynnNnnnnnnnnn
441714364517 ynnnNnnnnnnnnnn
441714365204 yyyyYnyyyyyyyyy
441714368181 yyyyYyyyyyyyyyy
441714517284 yyyyYyyyyyyyyyn
441714531297 yyyyYyyyyyyyyyy
441714821018 yyyyYyyyyyyyy
441714866403 yyyyYynnyyyyyyy
441714869970 yyyyYyyyyyyyyyy
441714905613 yyyyYyyyyyyyynn
441714910691 yyyyYyyyyyynyyy
441714911542 yyyyYyyyyyyyyyy
441714914139 yyyyYyyyyyynyyy
441714914993 yynyYyyyyyyyyyy
441714919347 nynnYyynyyyyyyy
441714919348 yyyyYyyyyyyyyyy
441714934333 yyyyYyyyyyyyyn
441714935105 yyyYyyynnnnnn
441714937456 yyyyYyyyyyyyyn
441714941868 nyynYyynynnnnn
441714951635 yyyyYyyyyyyyyy
441714954035 yyyyYyyyynyynn
441714954441 nyyyYyyynnnnnn
441714957776 yynyYyynynynn
441714958595 yyyyYyyyyyyyyn
441714992283 yynnYyynynnnnn
441714997948 yyyyYynnyyyynn
441714999937 yyyyYyyyyyyyyy
441715227300 nyyyYnnnnyyyyy
441715810053 yyyyYyynyyyyyy
441715811829 yyyyYyyyynyyyy
441715813452 yyyyYyyyyyyyy
441715819585 yyyyYyyyyyyyyy
441715830090 yynyYyyyyyyyyy
441715830118 yynnYyynynnnnn
441715830579 yyyyYyyyyyyyyy
441715830950 yyyyYyyyyyyyyy
441715831341 yyyyYyyyyyyyyy
441715831491 yyyyYyyynnnnnn
441715831606 yyyyYyyyyyyyy
441715831672 nnyyYnnyyyyyyy
441715831723 yyyyYyyyyyyyyy
441715831786 yyyyYyyyyyyyyy
441715831926 yyyyYyyyyyyynn
441715832030 yyyyYyyyyyyynn
441715832033 yynnYyynynnnnn
441715832036 yyyyYyyyyyyyyy
441715832044 yyyyYyyyyyyyyn
441715832051 yynnYyynynnnnn
441715832094 yyyYyynnyyyyy
441715832254 yyyyYyyynnnnnn
441715832257 yyyyYyyyyyyyyy
441715832686 yynyYyynynyyyy
441715832823 yyyyYyyyyyynyy
441715833423 yyyyYyyyyyyyyy
441715834928 yyyyYyyyyyyyyn
441715835106 yyyyYyyyynyynn
441715835885 yyyyYyyyyyyyyy
441715837228 yyyyYyyyyyyyyy
441715839123 yyyyYyyyyynyyy
441715839127 yyyyYyyyyyyyyy
441715839144 yynnEyyyyyyyyy
441715839178 yyyyYnyynyyynn
441715839269 yynnYynnnnnnnn
441715844948 yyyyYyyyyyyyyy
441715846354 yyyyYyyynyynnn
441715847716 yyyyYyynyyyyyy
441715892505 nyyyYyyyyyyyyy
441715893430 yyyyYyyynyynyn
441715894440 yyyyYyyyyyyyyy
441715895154 yyyyNyyynyyyyy
441715897725 nyyyEynynyynyy
441716000289 yyyyYyyyyyyyyy
441716001455 yyyyYyyyyyyyyy
441716021820 ynnnYyyyyyyyyy
441716075158 yyyyYyyynnnnnn
441716083746 yyyyYyyynnnnnn
441716294169 yyyyYynyyynyyy
441716647714 yyyyYyynyyyyyyy
441716969911 ynyyYyyyyyyyyy
441717083318 yyyyYyyynnnnnn
441717244174 yyyyYynyyyyyyy
441717247001 yyyyYynyyyynyy
441717249884 yynyYyyyyyyyyy
441717273693 yyyyYnyyyynB
441717275824 yyyyEynyyyyyyy
441717278625 yyyyYynnyyyyyy
441717278960 yyyyYyyyyyyyyy
441717279654 yyyyYyyyyyyyyy
441717279934 yyyyYyyyyyyyyn
441717301683 yyyyYyyyyyyyyy
441717305747 yyyyYyyynnnnnn
441717358147 yyyyYyyynnyynn
441717921708 nyyyYyyyyyyyyy
441717977100 yyyyYyyynnnnnn
441717977435 yyyyYyyynyyyyy
441717977550 yynnYyyyyyyyyy
441717977700 yyyyYyyyyyyyyy
441717977929 yynnYyynyyyyyy
441717978001 ynnnYyynyyyyyy
441717978101 yyyyYyyyyyyynn
441717978308 yyyyYyyyyynyyy
441717978401 yynnYnynynnnnn
441717978699 yyyyEyyyyyyyyy
441717978801 yyyyYyyyyyyyyy
441717978929 yyyyYyyyyyyyy
441718138080 yyyyYyyyyyyyy
441718231348 yyyyYynyyyyyyy
441718231712 yyyyYyyyyynyyy
441718237926 yyynYyyyyyyyyy
441718239695 yyynYyyynyyynn
441718239701 yyyyYyyyyyynyy
441718241435 yyyyYyyyyyyyyy
441718241566 yyyyYyyyyyyyyy
441718310125 ynnnYyynyyyyyy
441718310626 yyyyYnyyyyyyyy
441718312239 yyyyYyyyyyyyyy
441718312430 yyyyYyyyyyyyy
441718312469 yyyyYyyyyyyyyy
441718312575 yyyyYyyyyyyyy
441718314885 yyyyYyyyyyyyy
441718316016 yynyNyyyyyyyy
441718316109 yyyyYyyyyyyR
441718316112 yyyyYyyyyyyynn
441718316968 yynnYyynynnnnn
441718317107 yyyyEyyyyyyyyy
441718317907 yyyyYyyyyyyyyy
441718318102 ynyyYyyynyyynn
441718318129 yyyyYyyyyyyyyy
441718318237 yyyyYyyyyyyyy
441718318479 yyyyNyyyyyyyyy
441718319188 yyyyYyyyyyyyyy
441718362602 yyyyYyyyyyyyy
441718364331 yynyYyyyyyyyyy
441718374526 nyynNynnyyyyn
441718382001 yynnYyyyyyyyy
441718382046 yyynYyyyyyyyy
441718394580 yyyyYyyyyyyyy
441718398746 yyyyYyyyyyyyy
441718398958 yyyyYyyyyyyyy
441719175014 nynnYyyyyyyyy
441719175026 nyyyYyyyyyyy
441719225815 yyyyYyyyyyyyy
441719242704 yyyYyyynnnnn
441719308401 yyyYnnynyyyy
441719350034 yyyyYyyyynyyy
441719366709 yyyyYnyyyyyyy
441719366802 yynyYyyyyyyny
441719366810 yyyYyyyyyyyn
441719367230 yyyYnyyyyyyy
441719367428 yyyYyyyyyyyy
441719371783 yyyYyyyyyyyn
441719372925 yyyYyyyyyyy
441719375402 yyyYyyyyyyyy
441719375687 yyyyYynyyyyyn
441719376108 yyyyYyyyyyyyy
441719376741 nyyyYynnnyyyy
441719377505 yyyyYyyyyyyy
441719377918 yyyyEyyynnnnn
441719378069 yyyyYyynyyyyy
441719378795 yyyyYnyyyyyyy
441719381615 yyyyYynyyyyyy
441719382595 yyyyYynnnnny
441719579555 yyyyYyyyyyyy
441813127078 yyyyYyyyyyy
441813139624 yyyyYnyynnnn
441813164842 yyyyYnyyyyyy
441813165190 yyynNnnnnnnn
441813431324 yyyyYnynyyny
441814242209 yyyyYyynyyyy
441814530946 nyyyYnyynnyy
441815031152 yyynNnyyyyyn
441815532824 yyyyYyyyyyy
441815682401 yynyYyyyyyny
441815685368 yyyyYnynyyy
441816809801 yyyyYyyyyyyy
441816923910 yyyyYyyynnnn
441817600432 yyyyYyyyyyyn
441817811007 yyyyYyyyyy
441818030564 yyyyYyyyyyyy
441818779854 yyyyYyyynyyn
441818814802 yyyyYyyyyyyy
441819800670 yyyyYyyynnnn
441819891371 yyyyEyyyyyyy

No faxes were sent after April 2000.

3257

--
Posted via NewsDemon.com - Premium Uncensored Newsgroup Service
------->>>>>>http://www.NewsDemon.com<<<<<<------
Unlimited Access, Anonymous Accounts, Uncensored Broadband Access

==============================================================================
TOPIC: MI5 Persecution: Faxes Sent to Media1 (5432)
http://groups.google.com/group/sci.stat.math/browse_thread/thread/e967a455dfdee441?hl=en
==============================================================================

== 1 of 1 ==
Date: Sun, Nov 18 2007 7:39 am
From: MI5Victim@mi5.gov.uk

Faxes Sent to British Media logs

During the 1998-2000 period my records indicate that I sent at least 6,137 faxes to the British media,
of which 5,775 went via TPC's email-to-fax service and 362 were sent via fax-modem direct from my computer.
The actual figure is obviously higher since, for most of this period, whenever a recipient asked to be deleted
from my mailing list, I totally wiped all entries including that from the logfile.

441132420652 yyyyyYyyyyyyyyyyyyy
441132439387 yyyyyYyyynyyyyyyyy
441132445107 yyyyyYyyyyyynyyyyyy
441132455139 n
441132460037 yyyyyYyyyyyyyyyyyyy
441132461105 yyyyyYyyynyyynnnnnn
441132836586 yyyyyYynyyyyyyyyyyy
441142664375 nnyyyyYyyyyyyyyyyyy
441142769089 yyyyyyYyyyynnnyyyyyy
441142853159 yyyyyYyyynyynyyyyy
441159012850 y
441159363497 n
441159420433 ynnnnYyynnyyyyyyyyy
441159455243 n
441159527001 ynyyyYynnyyynnyynny
441159550552 yyyyyYyyyyyyynnnnn
441159822568 n
441162311123 y
441162511463 yyyyyYyyyyyyyyyyyyy
441162512151 yyyyyYyyyyyyyyyyyyy
441162512979 n
441162561303 yyyyyYyyyyyyyyyynB
441162640948 yyyyyYyyyyyyyyyyyyn
441162667776 yyyyyyyYyyyyyyyyyyyyy
441179226744 n
441179238323 yyyyyNynyyyyyyyyyy
441179279568 n
441179298612 yyyynYyyyyyynnnnnn
441179317463 y
441179722400 yyyyyyyYyyyyyyyyyyyyyy
441179732549 yyyyyyyYyyyyynyyyyyy
441179741537 yyyyyyyYnnynyyyyyyyyy
441179744114 yyyyyyyYyyyyyynyyyyy
441179843202 yyyyynYynynyyyyynyyy
441202297904 n
441203407129 n
441203551744 yyyyyyYyyyynyyyyyyyy
441203695110 y
441203696867 n
441203868202 nyyyyYyyyyyyyyyyyyy
441203868205 yyyyyYynyyynyyyyyyy
441206561199 yyyyyYynyyyyyyyyyyn
441209314345 yyyyyYyyyyyyyyyyyy
441212330173 n
441212331465 y
441212367220 y
441212434536 y
441213591117 yyynyNyynyyyyyynnyn
441214141120 y
441214148181 yyyyyYnnnnnnnnnnnnn
441214148241 yyyyyEnnnnnnnnnnnnn
441214148634 yyyyyYnnnnnnnnnnnnn
441214148847 yyyyyNnnnnnnnnnnnn
441214148900 nyyyyYnnnnnnnnnnnnn
441214155026 n
441214547622 n
441214723174 yyyyyYyyyyyyyyyyyyy
441216161011 yyyyyYynyyyyynnnnnn
441216251346 y
441216262041 yyyyyYyyyyyyyyyyyyy
441216344766 yyyyyYyyyyyyyyyyyyy
441216437239 y
441216666370 yyyyyYyyyyyynnnnnn
441216932753 yyyyyYyyyynyynnnnnn
441216961007 yyyyyYyyynyyyyyyyy
441217066210 yyyyyYyyyyyynnnnB
441217090205 y
441217115824 y
441217533111 nyynnYnyyyynyyyyyyy
441222223157 yyyyyYyyyyyyyyyyyyy
441222224947 y
441222229326 y
441222384014 yyyyyYyyyynyyyyyy
441222498151 n
441222552973 yyyyyNyyyyyyyyyyyyy
441222555286 yyyyyYyyyyyyyyyyyyy
441222555960 yyyyyYyyyyyyyyyyyyn
441222597183 ynyyyYyyyyyyyyynyyy
441222615966 y
441222665650 n
441222798555 n
441223235161 yyyyyNyyyyyyyyyyyyy
441223315052 nnnnnYnnnnnnnynyyyy
441223361508 yyyyyYyyyyyyyyyyyyy
441223460832 yyyyyYyyyyyyyyyyyy
441223467106 yyyyyYyyyyyyyyynyy
441224212163 y
441224633282 nyyyyYyynyyyyyyyyyy
441224637289 yyyynYynynnyyyyynyy
441224846800 yyyyyYyyyyyyyyyyyyy
441225448688 n
441225460709 y
441225775198 y
441227771558 nyyyyYyyyyyyynnnnnn
441228511195 nyynnYyyynyyyyyyyyy
441228810453 yyyyyYyynyyyyyyyyyy
441228818444 yyyyyYyyyyyyyynyyyy
441228819444 yyyyyYyyyyyyyyyyyyy
441229870008 ynnyyEyyyyyyyyyyyyy
441232246695 yyyyyYyyyyyyynyyyny
441232311740 y
441232338800 yyyyyYyyyynyyyyyyyy
441232338801 yyyyyYyyyyyyyyynyyy
441232381915 yyyyyYyyyyynynnnnyn
441232454406 n
441232541550 n
441232682111 y
441232682757 y
441232700029 y
441233641816 y
441233812707 n
441234218580 nyyynEyynyynnyyyyyn
441234270580 y
441235436899 y
441235524024 yyyyyYyyyyynyyyyyy
441235822746 y
441235835163 n
441237423333 yyyyyYyyyyyyyyyyyyy
441242255671 n
441242584270 y
441242621343 y
441242699666 yyynyYyyyyyyyyyyyyy
441243576456 yyyyyYyyyyynnnnnB
441243775878 y
441243786464 yyyyyyyyYyyyyyyyyyyyy
441244341677 n
441244678749 y
441245267228 yynnyynnYynyyyyyyyynyy
441245492983 nyyyyynEyyyyyyynnyyyy
441246582227 n
441247814974 yyyyyyyYnyyyyyyyyyny
441247818913 yyyyyyyYyyyyyyyyyyyyy
441248351443 yyynyyynYynyyyyyyynyyy
441252725007 n
441253301965 yyynyynyYyyynyyynyyyyy
441254680821 nyyyEyyyyyyyyyyyy
441256492599 y
441257450036 n
441273404280 yyyyyYyyyyyynyyynyy
441273430098 yyynyYyyyyyynyyyyyy
441273440229 y
441273486852 y
441274720375 n
441274728534 yynnnYnnnnnnnnnnnyn
441274771680 yyyyyYnyynynnynynB
441274828555 y
441279430009 n
441279431059 y
441279445289 yynnyYynnnnnyyyyy
441285647247 y
441292283665 nyynyYyyyyyyyynyyyy
441293560927 ynyyyYynnnnyyyyyyyy
441293565663 yyyyyYnyyyyyyyyynny
441293820517 n
441295270659 n
441296398988 yyyyyEyyyyyyyyyyyyy
441303272292 yyyyyNyyyyyyyyyyyyy
441303283874 yyyyyNyynyyyyyyyyyy
441305250052 yyyyyYyynyyyyyyyyyy
441305250910 yyyyyYyyyyyyyynyyy
441312202443 yynyyyNyyyyyyyyyyyynn
441312204344 yyyyyyYyyyyyyyyyyyyy
441312204819 y
441312254580 y
441312267420 yynyyyYyyynyyyyyyyyy
441312283220 n
441312284293 y
441312284333 n
441312285462 n
441312442674 n
441312444785 n
441313152906 y
441313177202 y
441313344473 y
441314462279 y
441315552869 n
441315575881 y
441315579699 y
441315583277 yyyyyYyyyynyyyyyyyy
441316621323 y
441316648001 n
441316688741 y
441322550553 nyyyyYyyyynyyyyyyyy
441322667633 y
441324630515 y
441325368824 n
441327361514 y
441332290794 yyyyyYyyyyyyyyyyyy
441332292229 yyyyyYyynynnnnnnnnn
441332386036 y
441334762661 n
441342850244 y
441343540142 nnnnnYynyyyyyyyyyyy
441344770990 y
441352759009 y
441352759821 yyyyyYnnnnnnnnnnnnn
441355262626 y
441372377526 y
441372465041 n
441373452888 y
441376521222 y
441382202188 yyyyyYynyyynyyyyyyn
441382454590 y
441382480230 yyyynNnnnnnnnnnnnnn
441382593252 yyyyyYyyyyyyynnnnnn
441387251121 y
441387265629 yyynyYnyyyyyynnynyn
441392425570 nyyyYyyyyyyyyyyyyy
441392428048 y
441392444433 yyyyyYyyyyyyyyyyyny
441392444644 yyyyyYyyyyyyyyyyyy
441397701007 yyyyEyyyyyyyyyyyy
441403234714 y
441412210283 yyyyyNyyyyyynyyyny
441412213595 n
441412264541 n
441412272090 n
441412481099 y
441412482362 yyyyyYyyynnnnnnnB
441412482470 n
441413003030 yyyyyYyyyyyyyynyyy
441413062265 yyyyyYyyyyyyyyyyy
441413062266 nynnnYynnnnnynnnnn
441413062322 yyyyyYyyyyynyynnnn
441413312126 yyyyyYyyyynyyyyyyy
441413325448 yyyyyNynnyyyyyyyny
441413327990 y
441413340614 ynnnnYyyyynyynyyyy
441413373031 y
441413382937 yyyyyYnyyynnnnnnB
441413382955 yyyyyYyyyyyyyyyyny
441413533292 yyyyyYyyyyyynnnnnn
441415322562 yyyyyYnyyynyyyyyyn
441415521344 yyyyyNyyyyyyyyyyyy
441418870963 nynyyYnyyyyyyyyyyy
441420475831 y
441420544044 yyyyyYyynyyyyyyyyy
441423522922 yyyyyYyyyyynyyyyyy
441423526294 n
441424870877 nnnyyYnnnnnnnnnnB
441428658971 yyyyyYyyyyyyyyyyyn
441428751057 y
441442877407 nnnynYyyyyyyyyyyyy
441442877409 nyyyyEyyyyyyyyyyyy
441443226500 n
441452300581 y
441452309491 yynyyYyyyyyyyyynyyy
441452423008 yyyyyYyyyyyynnnnnnn
441452529446 yyyyyYnyyynnnnnnnnn
441452652750 y
441453759311 n
441453890827 y
441454281094 y
441463243224 yyyyyYyyynyyyyyyyyy
441463715384 yyyyyYyynynyyynyyyy
441467632969 yyyyyYyyyyyyyyyyyyy
441472341967 yyyyyYyyyyyynynyyn
441473210887 yyyyyYyyyyyyyyyyyyyy
441473218447 n
441473233279 yyynyYyyyyyyyyyyyny
441473741200 yyyyyYynyyyyyyyyyyy
441477535756 y
44148149676 yyyyyYyyyyynnnnnnB
441481496768 yyyyyYyynyyyyyyyyyy
441481713557 yyynnEynyyyyyyyyyyy
441482226409 nyyyyEyyynyyyyyyyyy
441482586028 yyyyyYnyyyyyyyyyyyn
441482587067 yynyyEyyyynyyynyyy
441482848833 n
441483203378 n
441483304952 yyyyyYyyyyyyyyyyyyy
441483454443 yyyynYyyyyyyynynyn
441483506331 y
441483573686 y
441483764895 nyynyNnnnnnnnnnnnB
441484530604 n
441485600672 y
441488686900 n
441489589453 yyyyyYnnnyyyyyyyyyn
441491571635 y
441492535248 yyyyyYyynyyyynyynnn
441492543226 y
441494445400 yyyyyNyyyyyyyyyyyyy
441504260067 yyyyyYyyyyyyyynnnnn
441504269048 nyynyYyyynnnnnnnnB
441504311177 yyyyyEyyyyyyyyyyyyy
441505702333 yyyyyYyyyyyyyyyyyyy
441512362180 y
441514710330 yyyynNnnyyynnnynyyy
441514710333 yyyyyYyyyyyyynyyyyy
441516321698 y
441516325484 n
441517093389 yyyyyYyyyyyyyyynnny
441522511058 yyyyyYnyynyyyyyyyyy
441522511616 n
441522514162 yyyyyEyyyyynyyyynyn
441522549911 yyyyyYyyyyyyyyyyyyy
441524848787 yyyyyYyyyyyyyyyyyyy
441532443430
44153432569 yyynyYyynyyyyyyyyny
441534816817 yyyyyYyyyyynyyyyyy
441534887799 yyyyyYyyyyyyyyyyyyy
441536517390 yyyyYyyynyyyyyyny
441539445128 y
441543417154 y
441552560589
441553617137 n
441553766453 yyyyyYyyyyyyyyyyyny
441553767200 nyyyyYnyyyyynynnnyy
441582401214 yyynyYnnnnyyyyyyyn
441582401467 nyyyyYyyyyyyyyyyyyy
441582493486 y
441582746978 y
441584875900 yyyyyYnyynynnynyyyy
441590612624 y
441595695696 yyyyyYyyyynnnnnnnB
441603222602 y
441603415781 n
441603507723 y
441603612930 y
441603613483 nyyyyYnyyyyyynnnnnn
441603615494 yyyyyYyyyyyyyyyyyyy
441603630892 yyyyyYynyyyyyyyyyny
441603631032 yyyynYyyyyyyyyyyyyy
441603633631 yyynnYyyyyyyyyyyyyy
441603633692 yyyyyYyyyyyyyyyyyyy
441603666252 yyyyyYynyynyyyynynn
441603666353 yyyyyYyyyyyyyyyyyyy
441603667865 yyyyyYyyyyyyyyyyyyy
441603761245 yyyyyEyyyyyyyyyyyyy
441604230709 yyyyyYyyyyyyyyyyyyy
441604670635 y
441604795601 ynnnyYyyyyynynyyyny
441612281020 yyyyyYyyyyyyyyyyyyy
441612362427 n
441612364535 nyyyyYyyyyyyynyyynn
441612365804 yyyyyYyynyyyyyyyyny
441612369443 yyyyyYyyyyyynyyyyny
441612375353 y
441612889000 yyyyyNnynnnnnnyynny
441616836065 yyyyyYyyyyyyyyyyyyy
441618272029 yyyyyYyyyyyyyyyyyyy
441618320788 yyyyyYynynyyyyyyyyy
441618325351 y
441618328809 yyyyYyyyyyyyyyyyny
441618392066 n
441618398048 n
441618487824 yyyyyYyyyynyyyyyyy
441618720206 yyyyYyyyynyyyyyyyn
441618771005 yyyyYnnnnyyyynyyyy
441619692709 n
441622714000 yyyyyYyyyyyyyyyyyyy
441623427316 y
441624661411 yyyyyYyynyyyyyyyyyy
441625524510 y
441625583537 n
441625584344 yynnyYnnnnnnnnnnnnn
441631570057 yyyyyYyyyyyyyyynnyn
441634264701 n
441634830573 yyyyyYyyyyyyyyyyyyy
441634830930 n
441638665789 n
441642211356 yyyyyYyyyyyyyyyyyny
441642244595 n
441642566560 yyyyyYyyyyyynyyyyyy
441648763828 nyyyyYnnyyyyynnnnnn
441686623666 yyyyyYnyyyyyynnyny
441702345224 yyyyyYyyyyyyyyyyyy
441702556248 n
441703223388 yyyyyYyyyyynnnnB
441703232983 n
441703329404 y
441703335050 nyyyyYyyyyynyyyyyy
441703339648 yyyyyYyyyyyyyyyyy
441703339931 nyyyyYnnnnyyyyyyyy
441703630289 n
441705291709 y
441705358863 yyyyyYyynyyyynyyyy
441708727305 n
441708865870 n
441712097484 yyyyyYyynyyynyyyyy
441712110507 nyyyyYyyyynnnyyyyy
441712116270 y
441712174907 n
441712185857 yyyyyYyyyyyyyyyyy
441712186460 y
441712216341 nynnnYynnnnnnynnnn
441712220792 y
441712220832 yyyyyEyyyyyynnnnnn
441712221555 n
441712223172 nyyynYyyyyyyyyyy
441712225889 yyyyyYyynyyynnnnB
441712235631 nyyyyYnnnnnnnnnnB
441712241868 y
441712244452 y
441712272005 y
441712290352 yyyyyYyyyyyyyyyyyn
441712293395 yR
441712302691 n
441712302818 y
441712322302 ynyyyYyyynnnnnnnB
441712330335 n
441712330397 yyyyyYyyyyyyyyyyyy
441712332065 yyyyyYyyyyyyynnnnn
441712333161 n
441712333534 yyyyyYyyyyyyyyyyyy
441712333686 y
441712336706 yyyyyYynyyynyyyyyy
441712337899 y
441712343300 n
441712368136 nnnnnYnnnnnnnnnnB
441712388446 y
441712401153 y
441712402254 n
441712404171 yyyyyYnnnnnnnyyyyy
441712404399 n
441712405518 y
441712407517 y
441712428961 n
441712430542 n
441712447875 yyyyyYyyyyyyyyyyyy
441712478979 y
441712480133 y
441712492751 n
441712500212 y
441712500966 y
441712501204 n
441712512725 y
441712537406 n

5432

--
Posted via NewsDemon.com - Premium Uncensored Newsgroup Service
------->>>>>>http://www.NewsDemon.com<<<<<<------
Unlimited Access, Anonymous Accounts, Uncensored Broadband Access

==============================================================================
TOPIC: Solutions - Statistical Inference Casella & Berger
http://groups.google.com/group/sci.stat.math/browse_thread/thread/a72e54ebd4016e7d?hl=en
==============================================================================

== 1 of 1 ==
Date: Sun, Nov 18 2007 1:00 pm
From: milkman4

Hi I really need the solutions to the Casella & Berger 'Statistical Inference' 2nd edition exercises. If anyone could tell me where I can find them or could maybe send them to me (@ dj_dickens@hotmail.co.uk) it would be greatly appreciated.

==============================================================================
TOPIC: MI5 Persecution: Faxes Sent to Media2 (7607)
http://groups.google.com/group/sci.stat.math/browse_thread/thread/131af0a60e874b0a?hl=en
==============================================================================

== 1 of 1 ==
Date: Sun, Nov 18 2007 9:07 am
From: MI5Victim@mi5.gov.uk

441712545950 yynyyEyynnnyyyynnn
441712565326 yyyyyYyyyyyyynnnnn
441712611290 yyyyyYyyyyyyyyyyyy
441712613250 yyyyyNyyyyyyynyyyy
441712616023 y
441712626199 y
441712662563 y
441712671396 y
441712700618 y
441712706079 n
441712726653 n
441712734660 y
441712736421 y
441712748994 y
441712749630 y
441712765179 n
441712768633 n
441712780345 y
441712780504 n
441712784425 ynyyyYyyyyyyyyyyyn
441712786232 y
441712786941 yyyyyYyyyyyyyyyyyy
441712788480 ynnnnYynnynnnnnnB
441712822040 y
441712837037 n
441712842835 yynyYyyyyyyynnnnn
441712843374 yyyyyYnnnnnnnnnnB
441712843641 n
441712844494 n
441712847788 yyyyyEynnnnnnyynyy
441712896392 n
441712932435 yyyyyYyynnyyyyyynn
441712933098 y
441712933405 yyyyyYyynyyyyyyyy
441712933517 y
441712933587 y
441713066011
441713067737 y
441713067800 yyyyyYynyyyyyyyyyy
441713068116 yyyyyYyyyyyyynyyyy
441713068357 yyyyyYnyyyyyyyyyny
441713122601 n
441713128470 nnyyyYyyyynnnynyyy
441713169003 n
441713231582 y
441713241407 n
441713298377 n
441713313108 n
441713317382 y
441713331690 n
441713347411 n
441713386690 y
441713402019 n
441713446400 n
441713510220 y
441713518290 y
441713532310 nynynNnnnnnnnnnynn
441713536867 n
441713537526 n
441713538118 yyynyYnnyyyyyyyyyy
441713538355 yyyyyYyyyyyyyyyyy
441713538359 yyyyyYynyyyyyyyyyy
441713543728 n
441713548264 y
441713577458 y
441713592228 n
441713735768 y
441713740645 n
441713748741 yyyyyYyyyyyyyyyyyy
441713776103 yyyyyYyyyyynyyyyyy
441713790543 y
441713793866 n
441713794204 n
441713797707 y
441713816903 n
441713825905 n
441713830044 y
441713830273 y
441713830448 n
441713834855 n
441713841781 n
441713853708 n
441713865002 yyyyyYyynyyyyyyyny
441713872764 n
441713883737 yyyyyYyyyyyyynnnnn
441713888172 nnnnnEnnnnnnnnnnnn
441713889958 yyyyyYyyyyyyyyyyyy
441713919111 y
441713932099 y
441713937461 y
441713966466 y
441714001428 y
441714012537 n
441714030668 n
441714044167 y
441714052332 y
441714057062 yyyyyYnnnnyyyyyyyy
441714057064 y
441714057163 yyyyyYyyynyyynnnnn
441714057784 y
441714075700 yyyyyYyyyyynyyyyyy
441714100906 y
441714100966 yyyyyYyyyyyyynyyyy
441714127379 y
441714127771 y
441714130340 n
441714138302 nyyyyYyyyyyynyyyyy
441714138303 yyyyyYyyyyyyynnnn
441714140262 n
441714165379 y
441714216006 yyyyyYyyyyyyyyyyyy
441714216522 y
441714300924 ynnnnYyyyyyyyyyyyy
441714301384 y
441714301779 n
441714341197 yyyyyYyyyyyynyyyny
441714361425 n
441714362665 y
441714363562 n
441714363986 n
441714365290 n
441714367218 y
441714376886 y
441714384512 n
441714397318 n
441714700062 yyyyyYyynyyynyynny
441714701062 yyyyyYyyyyyyyyyyyy
441714807662 y
441714822293 y
441714822441 n
441714861132 ynyyyYyyyyyynyyyyy
441714866565 n
441714872908 yyyyYyyyyyyyyyyyy
441714900436 y
441714901255 yyyyyYyyyyyyyyyyy
441714904706
441714918212
441714946652
441714999377
441714999751
441715058220
441715229623
441715372661
441715376004
441715376721
441715381330 ynnyNnnnnnnnnnnn
441715386242 yyynYyyyyyyyyyyy
441715387872
441715388625
441715427921 yyyyyYyyyyyyyyyyy
441715802338
441715805455 nnnnnYyyyyyyynnnn
441715805547 yyyyyEyyynyyyynyy
441715807725 nyyyyNnnnnnnnnnnn
441715814431 yyynyYyyyyyyyyyyy
441715817000 yynynYnnnnnnnnnnn
441715828240
441715830519 yyyyyYyyyyyyyyyyy
441715830701
441715831519
441715833441
441715833563
441715833769 ynyyyYyynyyyynyyn
441715835901
441715836608 yyyyyYynynyyyyyyy
441715836868 yyyyyYyyyyyynyyyy
441715837073
441715838353
441715839504 yyyyYyyyyyyyyyyn
441715844381
441715867187
441715946700
441716001058
441716030095
441716080020
441716080114
441716083757
441716118577
441716201594
441716305509
441716309934
441716311070
441716313214
441716315084 yyyyyYyyyyyyynnnn
441716315119 yyyyyYyyyyyyyyyny
441716330244 yyyyyYyyynnynynyy
441716348842
441716360632
441716361053 nyyyyYnnnnnnnnnnn
441716362628 yyyyyYnyyyyyynnnn
441716365375
441716365668
441716371439
441716371630 yyyyyYyynynnnnnnn
441716372127
441716372748 nyyyyYyyyyyyynnn
441716373925 yyyyyYnyynyyyyyyy
441716376868 yyyyyNyynyyyyyyyn
441716539393 yyyyyYyyyynynyyyy
441716643349
441716968996
441717003752 yyyynYyyyyyynnnnn
441717003979 nnyyyYynnnnnnyyn
441717040313
441717053030 yyyyyYyyyyyyyyyny
441717131840 yyyyyYyyyyyyynnn
441717136161
441717228512
441717238585 yynyyYynnnnnnnnnn
441717239742 yyyyyYyyyyyyyyyy
441717240404 yyyyyYyyyyyyynnnn
441717276268
441717295098
441717305851
441717340561
441717341792 yyyyyNyyyyyyyyyy
441717345030
441717385509
441717386619 yyyyyYnnynyynnnnn
441717471414
441717650612 yyyyyYyyyyyyyyyy
441717653774 yyyyyNyyyyyyynnnn
441717654409 yyyyyYyyyyyyyyyyy
441717655838 yynyyYyynyyynyyyy
441717666100 nyyynYnnnynyyyyyy
441717673959
441717823252
441717825658 yyyynNyynyyyyynyy
441717829902 ynnnnYyyyyynyyyyy
441717920259
441717946584
441717987710
441718136001
441718149899
441718151177 yyyyyYyyyyyynyyyy
441718215387
441718219352 yyyyyYyyyyyyyyyy
441718231621
441718277001 ynnnnYynynnnnyyyy
441718277002 yyyyyYyyynyyyyyyy
441718277710 yyynyNnnnnynyyyyy
441718277721 yyynyYnyyyyynyyyy
441718287269 yyyyyYyynnnnnnnnB
441718288692
441718306220
441718311746 yyyyYyyyyyyyyyny
441718312035
441718312483
441718314468
441718317477
441718317991
441718330381
441718331383
441718339136
441718370211
441718371173
441718371192 yyyyyYyyyyyyyyyyy
441718374812
441718374942
441718375118 yyynyYyyyyyyyyyyy
441718377171
441718378143 yyyyyYyyyyyyyyyyy
441718395805
441718738200 yyyyyNnnnnnnyyyyy
441718959020 yyyyyYyyyyyyyyyyy
441718969007 yyyyyYyyyyyynnnnB
441719161509
441719170523
441719226545
441719227288
441719227966 yyyyyYyyyyynyyyny
441719250469 yyyyyYyyyyyyyyyyy
441719250795
441719282067 yyyyYyyyyyyyyyyy
441719282728
441719285037 yyyyyYyyynyyyyyyy
441719285440
441719286974
441719288144
441719288405 yyyyyYyyynyyyyyyy
441719288476 yyyyyYyyyyyyynnnn
441719303092
441719308499 yynyyYyyyyyyyyyn
441719310429
441719351546
441719356510
441719362229 yyyyyYyyyyyyyyy
441719362689 yyyyyNynyyynyny
441719371479
441719371767
441719372849
441719373745 yyyyyYnnyyyyyyy
441719375287
441719377896 yyyyYynyynyyyyy
441719383165 yyyyyYyyyyyyynnB
441719389066
441719389751
441719550110
441719561435
441719561469
441719637192 yyyyyEyyyyyyyyyy
441719733252
441719736380 yyyyyYyyyynyyyyn
441719737355
441719766478
441722415102 yyyynYyyyynyyyyyy
441723500117
441723500254
441727834456 yyyyyYyyyyynyyyyy
441732350570
441732362541
441732369201 yyyyyYyyyyyyyyyyn
441732463918
441732763285
441732770049
441732771160
441733269424 yyyyyYyyyyyyyyyyn
441733281445 yyyynYnyyyynnyyny
441733464222
441733897629
441734351232
441734504340
441734568211
441737373848
441743247701
441743271702 yyyyyYyyyyyyyyyyy
441745815903
441745855534
441747855722 ynnnnYnyyyyyyyyny
441752222778
441752234595 yyyyyYyyyyyyyyyyy
441752255962 yyyyyYyyyyyyyyyyy
441752333444 ynnnnNyyyyyyyyynn
441752670730 yyyyyYyyyyyyyyyyy
441753512277 yyyyyYyyyyyyyyyyy
441753571617
441762391896 ynnnnYyyyynyynnnn
441766762734
441767692365
441772201917 yyyynNyyyyyyyyyyy
441772618356 yyyyyNyyyyyyyyyyy
441782289115 yyyyyNyyyyyyyyyyy
441782641121 yynnnYynnnnnnnynn
441782744110 yyyyYyynyyyynyny
441782747777 nnnnYnnnnnnyyyy
441782812428
441789263102 yyyyyYyyyyyyyyyny
441792468194 yyynYnnnnnyyynnn
441792475264
441792511171 yyyyYnyyyyynyyyy
441792511965 nnnnYnynyyyynnnn
441793411510
441793414606
441793513650 yyyyyYyyyyyynyyyy
441793524318
441793886182
441796474007 yyyyyYyynyyyyyyyy
441812001751
441812034067
441812245769
441812450845
441813121930 ynnnnYnnnnynnnyyy
441813126632
441813320495 nynnnYynnnnnnyyyy
441814134503
441814403159
441814442313 yyyyyYyyyyyynnnB
441814467695
441814794040 yyyyyYnynyyyynyyy
441815093190
441815633029
441815670605
441815762782 yyyyyYyyynnyynnnn
441815764284 yyyyyYynyyynynynn
441815767624 yyyyyYyyyyyynnyy
441815990984 yyyyyEyyyyyyynyyy
441815990989
441816006119 yyynyNnnnnnnnnnnn
441816524005
441816528932
441816592409 yyyynYynyyyyyyyyy
441816711722
441816813937 nnnnnEnnnnnnnynnn
441816874309
441817401741
441817404622 yyyyyYyyyyyyyyyyy
441817413819 yyyyyYyyynyynnnnB
441817491647 nnnnnEnnnnnnnnnnn
441817492831 yyyyyYynnnnnnnnnB
441817496734 yynyyYnnnnyynyyyy
441817496972 ynnnnYyynnyyyyyyy
441817497520 nnyyyYnyyyyyynnnn
441817497554 yyyyyYyyyyyyyyyyy
441817497872 nyynYyyyyyyynyyy
441817498622 yyyyNnnnnnnnnnnn
441817499016 yyyyNnnnnnnnnnnn
441817499259 yyyyYyyyyyyyyyny
441817524398 yyyyYyyyyyynyyyy
441817525915 yyyyYyynnnyyyyy
441817597739
441817600588
441817600973
441817611502
441817819794
441818008005 yyyyYyyyyyynyyyn
441818139700 nnnyYnyyyyynyyyn
441818339215
441818527211
441818547476
441818582049
441818788905
441818815151 yyyyYyyyyyyyyny
441818839215
441819000705
441819059729
441819070073
441819431198
441819802041
441819813779 yyyyNynnnnnnnnnB
441819833846
441822614405 nyyyYnnnynnynnnB
441823321044 yyyyYyyynyyyyyyn
441823332539 yyyyYyyyyynyyyny
441823332862
441851704270
441865716418
441865791347 yyyyYyyynyynyyyn
441865843971
441889590588
441892518028
441896759494 yyyyYyyyyyyyyyyy
441902755163 yyyyYyyyyyyynnnn
441902772415
441902838266 yyyyYyyyyyyynnnn
441904488878 yynyYyynyynynyy
441904523698
441904610067 nyyyYnnnnnnnnnn
441904610937 nyyyYyyyyyyyyyyy
441904611112
441904651642
441905748006 ynyyYyynyyyyyyyyy
441905766293
441908564893 yyyyYyyyyyyyyyyy
441908694008 yyyyYyyyyyyynnnn
441912210112 yyyyYyyyyyyyyyy
441912220013
441912321710
441912325082 yyyyYyyyyyynyyyy
441912612302 yyyyYyyyyyyyyyyy
441912618571 yyyyYynyyyyyyyyn
441914775660 yyyyYyyyyyynyyny
441914880933 yyyyYyyyyyyyynnn
441914888611 nyyyYyyyyyyyyyyy
441914889222 yyyyYyyyyyyyyyyy
441915670888 nnnnNnnnnnnnnnnn
441920487056
441923425457
441923664606
441924466522
441926403958
441926424760 yyyyYyyyyyyyyyyy
441926831532
441932832532
441932854750
441932859661 yyyyYyyyyyyyyyy
441942884397 yyyyNyyyyyyyyyyy
441946724889
441949836583
441952291035
441962713134
441962861186
441962861646
441970627206 ynnnYnnyynyynnnB
441978758565 yyyyYyyyyyyyyyyy
441978759701 yyyYyyyyyyyyyn
441983821690 yyyYyyyyyyyyyyn
441984623901 yynYyyyynyynnnn
441992555647

No faxes were sent after April 2000.

7607

--
Posted via NewsDemon.com - Premium Uncensored Newsgroup Service
------->>>>>>http://www.NewsDemon.com<<<<<<------
Unlimited Access, Anonymous Accounts, Uncensored Broadband Access

==============================================================================

You received this message because you are subscribed to the Google Groups "sci.stat.math"
group.

To post to this group, visit http://groups.google.com/group/sci.stat.math?hl=en

To unsubscribe from this group, send email to sci.stat.math-unsubscribe@googlegroups.com

To change the way you get mail from this group, visit:
http://groups.google.com/group/sci.stat.math/subscribe?hl=en

To report abuse, send email explaining the problem to abuse@googlegroups.com

==============================================================================
Google Groups: http://groups.google.com?hl=en

Statistika For Life

Rabu, 21 November 2007

15 new messages in 7 topics - digest

Selasa, 20 November 2007

20 new messages in 11 topics - digest

Senin, 19 November 2007

16 new messages in 14 topics - digest

Arsip Blog

Mengenai Saya