"R. Stegers" <[EMAIL PROTECTED]> wrote in message news:<a3kc0p$ae3$[EMAIL PROTECTED]>...
> I'm trying to understand some medical paper and they used both Log-rank and
> Mantel-Haenszel. Could anybody briefly explain what is measured by these
> specific test
"R. Stegers" <[EMAIL PROTECTED]> wrote in message news:<a3kc0p$ae3$[EMAIL PROTECTED]>...
> I'm trying to understand some medical paper and they used both Log-rank and
> Mantel-Haenszel. Could anybody briefly explain what is measured by these
> specific test
I'm trying to understand some medical paper and they used both Log-rank and
Mantel-Haenszel. Could anybody briefly explain what is measured by these
specific tests? Why are they used (in general) en what a calculated value
stands for.
Thanks in advance,
Hi everyone, I have this problem and I was
wondering whether anyone can help me.
I have data on the number of accidents occuring on
two different roads, two different years, three different time periods
(of unequal length), given a number number of vehicles
for each occasion. (for example
nd the formula's for the mean and
>> variance of a log-logistic distribution.
>>
>> Thanks in advance,
>>
>> Kris
>
>
>=
>Instructions for joining and leaving this list and remarks about
p 37 of www.causascientia.org/math_stat/Dists/Compendium.pdf
Kris Bogaerts wrote:
>
> Dear All,
>
> I am looking for a reference where I can find the formula's for the mean and
> variance of a log-logistic distribution.
>
> Th
Dear All,
I am looking for a reference where I can find the formula's for the mean and
variance of a log-logistic distribution.
Thanks in advance,
Kris
=
Instructions for joining and leaving this list and remarks
On 31 Jul 2001, ToM wrote:
> what is the opposite of a log?[logarithm]
An antilog [properly, antilogarithm]. Equivalently, 10 to that power
(if, as in your example, you are taking logarithms to the base 10); or
e to that power (if you are taking natural logarithms), which is a
ote in message
[EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
> hi
>
> what is the opposite of a log?
>
> If you do lg10 of 3 in spss, it gives you a number. how can i take
> this number and have as a solution the in
hi
what is the opposite of a log?
If you do lg10 of 3 in spss, it gives you a number. how can i take
this number and have as a solution the initial one (3)
its easy but i cannot remeber
=
Instructions for joining and leaving
"H. Noedl" wrote:
> Unfortunately my knowledge of statistics is rather limited to the basics
> (i.e. regression, t-test etc.). I tried by transforming the drug
> concentration into NLog (LN(x)) and the response into probits
> (NORMSINV(x)+5) and doing an ordinary linear regression (y=a+bx) to
>
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I don't know where that came from. I studied from Finney as well. I
guess I'm suffering from old-timer's disease. :-)
--46D07
s popular because it
fits many natural models; the math models are powerful; and
you don't need a table to look up the logit (log of P over 1-P ).
Zero (or 100%) is a problem for either transformation.
Folks sometimes adjust their cell-totals by the equivalent
of "1/2 of an event.&qu
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The transform from Z scores to probits uses a constant 6 translation,
not 5. I don't know if that solves your problem, but it might elimin
I am a medical Doctor primarily working in malaria research in Thailand.
We are doing in vitro tests to assess the drug sensitivity of malaria
parasites. For evaluation of the results I use the log-probit model
(which is the standard evaluation method for malaria in vitro test) of
SPSS 10, which
> Does anybody know how to calculate the sample size needed to prove
> EQUIVALENCE, not difference of two treatments concerning survival data
> (log-rank test, cox regression).
>
> Thanks Bernd
>
If you were willing to use proportions or means, you could use our program
PASS (a
On Fri, 16 Jun 2000 15:03:32 +0200, Bernd Genser
<[EMAIL PROTECTED]> wrote:
> Does anybody know how to calculate the sample size needed to prove
> EQUIVALENCE, not difference of two treatments concerning survival data
> (log-rank test, cox regression).
"Bio-equivalence&qu
On Fri, 16 Jun 2000, in reply to Bernd Genser's query
> > Does anybody know how to calculate the sample size needed to prove
> > EQUIVALENCE, not difference of two treatments concerning survival
> > data (log-rank test, cox regression).
Robert Dawson wrote:
> I
> Does anybody know how to calculate the sample size needed to prove
> EQUIVALENCE, not difference of two treatments concerning survival data
> (log-rank test, cox regression).
Infinite?
The only situation in which I would consider a test as proving the
equivalence of
two p
Does anybody know how to calculate the sample size needed to prove
EQUIVALENCE, not difference of two treatments concerning survival data
(log-rank test, cox regression).
Thanks Bernd
===
This list is open to everyone
Bojanowscy wrote:
>
> Hello
>
> I would be very grateful if anyone of you could give me (in short) a list of
> assumptions about data (dimensions, frequencies in contingency table,
> distribution etc) under which one can perform Loglinear analysis (ML
> estimation).
Log-li
Hello
I would be very grateful if anyone of you could give me (in short) a list of
assumptions about data (dimensions, frequencies in contingency table,
distribution etc) under which one can perform Loglinear analysis (ML
estimation).
...And, if there are any diffrences, in Logit modelling.
I've
Re: Ordinal log-linear model
It's me again
Following your suggestions downloaded LEM. The program is working (I
examined featured examples which I downloaded too).
I also downloaded zipped manual for LEM. The filename was: MANUAL.PS and in
README.TXT was written, that this is a "pos
Thank you all.
Especially to Jan de Leeuw and Frank Isackson who sent me the PDF version of
LEM manual.
Again
Thank you very much
Michal Bojanowski
===
This list is open to everyone. Occasionally, less thoughtful
people
Michal Bojanowski posted (in part)
>What is that - POSTSCRIPT viewer / printer?
>Or simply
>How can I view or print the LEM manual?
I suspect that most members of this list know if they have a PostScript
printer capable of rendering the LEM manual. For those who do not have
such a printer I hav
In article <[EMAIL PROTECTED]>,
[EMAIL PROTECTED] says...
>
>In article <8gr7ab$jk$[EMAIL PROTECTED]>, "Buoy"
><[EMAIL PROTECTED]> wrote:
>
>>Following your suggestions downloaded LEM. The program is
>working (I
>>examined featured examples which I downloaded too).
>>I also downloaded zipped manu
In article <8gr7ab$jk$[EMAIL PROTECTED]>, "Buoy"
<[EMAIL PROTECTED]> wrote:
>Following your suggestions downloaded LEM. The program is
working (I
>examined featured examples which I downloaded too).
>I also downloaded zipped manual for LEM. The filename was:
MANUAL.PS...
>
>What is that - POSTSCR
It's me again
Following your suggestions downloaded LEM. The program is working (I
examined featured examples which I downloaded too).
I also downloaded zipped manual for LEM. The filename was: MANUAL.PS and in
README.TXT was written, that this is a "postscript" format (???) which can
be printed
Thanks to all of you for all suggestions. I just visited websites you
mention and
I'm downloading LEM right now. Tomorrow I'm going to work on it.
Thanks again
Michal Bojanowski
===
This list is open to everyone. Occasi
In article <8gjali$hh1$[EMAIL PROTECTED]>, [EMAIL PROTECTED]
says...
> Hello to all.
>
> I'm just studying Masako Ishii-Kuntz's "Ordinal log-linear models" (SAGE
> 1994 no.97).
> What I wanted to do is to practice fitting by SPSS 8.0 various types of
&
ect and column-effect models and also
> > uniform association models by SPSS GENLOG procedure. I've got the problem
> > with Row-and-Column effects model (RC model), I don't know how to fit it
> > using SPSS.
> >
> > The model for a two-way table is
> >
fit it
> using SPSS.
>
> The model for a two-way table is
>
> LOG(F ) = M + L + L + B*U *V
> ij ij i j
>
> where:
> L = lambda
> M = constant
> B = beta
> in standard notation and:
> UV
> ij
> are est
Hello to all.
I'm just studying Masako Ishii-Kuntz's "Ordinal log-linear models" (SAGE
1994 no.97).
What I wanted to do is to practice fitting by SPSS 8.0 various types of
models mentioned in the book.
There is no problem to fit both row-effect and column-effect mod
Quick reaction, Rick:
For the "statistically challenged" I'd be tempted to try the following;
maybe there are reasons you know of in the situation that would make it
... umm ... impolitic.
Anyway: It looks as though no one is interested in pursuing
interactions among the several categ
Hi Don,
Thanks for the response. Comments, clarification, and questions below.
--- You wrote:
On 3 May 2000, Richard M. Barton wrote:
> Suppose Y does not appear to be normally distributed, but Z=ln(Y) does.
>
> I do a linear regression of Z on X, which is dichotomous (0,1).
>
> 1) In simp
On 3 May 2000, Richard M. Barton wrote:
> Suppose Y does not appear to be normally distributed, but Z=ln(Y) does.
>
> I do a linear regression of Z on X, which is dichotomous (0,1).
>
> 1) In simple terms, what does the unstandardized regression
> coefficient b tell me about the relationship
Forgive what may be simplistic questions:
Suppose Y does not appear to be normally distributed, but Z=ln(Y) does.
I do a linear regression of Z on X, which is dichotomous (0,1).
1) In simple terms, what does the unstandardized regression coefficient b tell me about the relationship between X and
L.S.:
I am a graduate student (Drs degree) in finance & investments.
I have the following problems and I got stuck.
My data are the returns on a quarterly basis of leveraged buyout funds in
the USA from 1989 to 1999 (40 quarters). I have bought this data in two
groups with in one group funds s
On Fri, 24 Mar 2000 Llorenç Badiella <[EMAIL PROTECTED]> wrote:
> Is it a problem that there exists colinearity between
> variables when performing a Log-Reg? If so, how avoid it?
It can be a problem, or for that matter several problems. Depends on how
severe the collinearity is,
On Fri, 24 Mar 2000 15:28:38 GMT, [EMAIL PROTECTED] wrote:
> Is it a problem that there exists colinearity between
> variables when performing a Log-Reg? If so, how avoid it?
Sure, it is about the same problem as exists for OLS regression. If
you really want to *avoid* it, you h
Hi all,
Is it a problem that there exists colinearity between
variables when performing a Log-Reg? If so, how avoid it?
Thanks in advance,
Llorenç Badiella
[EMAIL PROTECTED]
Sent via Deja.com http://www.deja.com/
Before you buy
Dear STATISTICA friends,
I cannot figure out the meaning of the survival rate (pi 1 and pi 2) of
the log-rank test. (Power Analysis Software).
So my questions are:
a) How is the log-rank test defined? What does this test actually
compare? (The mean of the underlying distribution or the whole
On Wed, 22 Dec 1999 11:27:21 +0100, Miguel Verdu <[EMAIL PROTECTED]>
wrote:
> Why log-linear models can reflect a significant partial association
> between factors when none of the cells are significant?.
- I'm not 100% sure about your question, but I think the answer is,
&q
Hi -
Why log-linear models can reflect a significant partial association
between factors when none of the cells are significant?.
Thank you very much
Miguel Verdu
"Donald F. Burrill" wrote:
> On Fri, 26 Nov 1999, Frank E Harrell Jr wrote:
>
> > Beware - you can't just anti-log the mean and s.d. The median
> > unlogged value is the antilog of the mean of the logged values.
>
> That's interesting. The an
On Fri, 26 Nov 1999, Frank E Harrell Jr wrote:
> Beware - you can't just anti-log the mean and s.d. The median
> unlogged value is the antilog of the mean of the logged values.
That's interesting. The antilog of the mean of log(X) is the geometric
mean of X. Is th
On Fri, 26 Nov 1999, Mr. SISAVATH Sourith wrote:
> Thanks for the advice.
> What I meant about the least square methods is as follows:
> If I calculate the mean and the variance of y=log(x)
> using the "standard" equations I mentioned in the previous mail
> mea
Beware - you can't just anti-log the mean and s.d. The median unlogged value is
the antilog of the mean of the logged values. The mean unlogged value is something
like exp(mean unlogged + .5sigma2) where sigma2=sd of logged values. This is also
a very assumption-laden approach (logarithm
On Wed, 24 Nov 1999, Mr. SISAVATH Sourith wrote:
> I have a data sample of grains and the histogram of the
> grain size makes me think that the distribution is log-normal.
> Is it then reasonable to approximate the density function by a
> log-normal distribution, whose variance an
Hello
I have a data sample of grains and the histogram of the
grain size makes me think that the distribution is log-normal.
Is it then reasonable to approximate the density function by a
log-normal distribution, whose variance and mean value
has been calculated from the histogram, i.e.
mean
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