Dear Colleagues,
We have received many requests for extending the IAT-2001 submission
deadline. After further consideration, we decide to extend the
submission deadline to April 1, 2001.
On-Line Submission is encouraged and preferred. Please use the
Submission Form at the IAT-2001 webpage:
"htt
Re assumptions for inference...
- Forwarded message from Robert J. MacG. Dawson -
What is needed in the small-sample case is outside _knowledge_ (not
"well, it _might_ be true" or "in this discipline we usually assume..."
assumptions!) about the distribution - without this we should not
W. D. Allen Sr. <[EMAIL PROTECTED]> wrote in message
nH9u6.6370$[EMAIL PROTECTED]">news:nH9u6.6370$[EMAIL PROTECTED]...
> A common mistake made in statistical inference is to assume every data set
> is normally distributed. This seems to be the rule rather than the
> exception, even among profess
On Thu, 22 Mar 2001 08:23:54 -0500, Bruce Weaver
<[EMAIL PROTECTED]> wrote:
>
> On 21 Mar 2001, Awahab El-Naggar wrote:
>
> > Dear Colleagues
> > I have been using "test-retest" method for calculating reliability by
> > applying the Pearson Product Moment (PPM) analysis. However, I have been
> >
The UMDNJ Biometrics Division in co-sponsorship with the NJ Chapter of the
ASA
presents
Title: Meta-analysis - What is Possible, Sensible and Practical ?
Speaker: Prof. Douglas Altman
Professor of Staistics in Medicine
ICRF Medical Statistics Group, Centre for Statis
On Fri, 23 Mar 2001, Alan McLean wrote:
> The second sentence here ensures that generalisability to a population
> IS an issue for statistics. And a big issue, usually overlooked.
>
It is not a statistical issue with a non-random sample; it is a matter of
experimental judgement
> For that ma
Paul's comment is very apt. It is very important to consider whether
a consistent error should or should not count against reliability.
In some cases, a constant positive or negative bias should not matter.
For example, one might be willing to standardize each measure before
using it in statistic
From: [EMAIL PROTECTED] (Simon, Steve, PhD)
>For a lognormal distribution, the left tail can often be approximated by a
>triangular distribution. The median of a triangular distribution is the
>upper limit divided by the square root of 2.
>
>There are more sophisticated approaches, of course, to
[EMAIL PROTECTED] writes:
>"Analyte concentration levels less than the limit of detection
>were assigned a value equal to the detection limit divided by the
>square root of 2 for calculation of geometric mean values.
>
>There must be a simple reason for the sqrt(2), but I'm not seeing it.
>Can so
Dear Colleagues:
Third biennial Midwest Conference on Teaching Statistics (MCOTS-3) will be held
June 27-29, 2001. The conference will start on Wednesday afternoon and end on
Friday afternoon.
Authors wishing to contribute a 30 minute paper should submit an abstract
(e-mail submission preferre
The second sentence here ensures that generalisability to a population
IS an issue for statistics. And a big issue, usually overlooked.
For that matter, many applications of statistics do use sampling, not
random assignment (market surveys, for example) and in these
applications Dennis' observtio
given random assignment the generalizability of results to a population is
not an issue for statistics. It's a question of what a plausible
population is, given the procedure for obtaining subjects
On Thu, 22 Mar 2001, dennis roberts wrote:
>
> using and interpreting inference procedures under
here is my entry for the most common mistake made in statistical inference ...
using and interpreting inference procedures under the assumption of SRS
simple random samples ... when they just can't be
this permeates across almost every technique ... and invades almost every
study ever p
See Welch, W.J. (1982). Branch-and-bound search
for experimental designs based on D optimality and
other criteria. Technometrics. 24. 41-48.
Salem S Reyen wrote:
>
> Hi,
>
> Is d-optimal experiment designs a NP-complete problem?
> Does anyone know where I can acess the paper that shows
This may be a simple question on how to handle non-detects in analysis:
The CDC just released an important survey of chemicals in humans:
http://www.cdc.gov/nceh/dls/report/default.htm
One part of the data analysis piqued my interest, how non-detects were handled:
On the following page:
http://ww
"W. D. Allen Sr." wrote:
>
> A common mistake made in statistical inference is to assume every data set
> is normally distributed. This seems to be the rule rather than the
> exception, even among professional statisticians.
>
> Either the Chi Square or S-K test, as appropriate, should be cond
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