Ronny Richardson wrote:
A few weeks ago, I posted a message about when to use t and when to use z.
In reviewing the responses, it seems to me that I did a poor job of
explaining my question/concern so I am going to try again.
I have included a few references this time since one
At 04:14 AM 12/10/01 +, Jim Snow wrote:
Ronny Richardson [EMAIL PROTECTED] wrote in message
[EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
A few weeks ago, I posted a message about when to use t and when to use z.
I did not see the earlier postings, so forgive me if I repeat advice already
Dennis Roberts wrote:
this is pure speculation ... i have yet to hear of any convincing case
where the variance is known but, the mean is not
What about that other application used so prominently in texts of
business statistics, testing for a proportion?
But then you should use a binomial (or hypergeometric)
distribution.
Jon Cryer
p.s. Of course, you might approximate
by an appropriate normal distribution.
At 11:39 AM 12/10/01 -0400, you wrote:
Dennis Roberts wrote:
this is pure speculation ... i have yet to hear of any convincing
case
where
Dennis Roberts wrote:
this is pure speculation ... i have yet to hear of any convincing case
where the variance is known but, the mean is not
A scale (weighing device) with known precision.
=
Instructions for joining and
I always thought that the precision of a scale was
proportional
to the amount weighed. So don't you have to know the mean
before you
know the standard deviation? But wait a minute - we are trying
assess
the size of the mean!
Jon Cryer
At 03:42 PM 12/10/01 +, you wrote:
Dennis Roberts wrote:
You have probably thought of this, but the age old standard is the Chi
Square test.
One thing about empirical distributions is that they may not be one of
the standard forms. This is why the Jackknife method and then later the
Bootstrapping methods were developed. Thus you can extract the
the sample mean of the dichotomous (one_zero, dummy) variable is known, It
is the proportion.
Gus Gassmann wrote:
Dennis Roberts wrote:
this is pure speculation ... i have yet to hear of any convincing case
where the variance is known but, the mean is not
What about that other
Art Kendall wrote:
(putting below the previous quotes for readability)
Gus Gassmann wrote:
Dennis Roberts wrote:
this is pure speculation ... i have yet to hear of any convincing case
where the variance is known but, the mean is not
What about that other application used so
Usually I would use software. As I tried to show is the sample syntax I posted
earlier, it doesn't usually make much difference whether you use z or t.
Gus Gassmann wrote:
Art Kendall wrote:
(putting below the previous quotes for readability)
Gus Gassmann wrote:
Dennis Roberts
Hi!!
Thanks for your reply...do you mean that Jackknife and Bootstrapping methods
area also some kind of goodness-of-fit tests??
Cheers,
CCC
Clay S. Turner [EMAIL PROTECTED] wrote in message
[EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
You have probably thought of this, but the age old
Hello Chia,
No actually they are used to extract the distribution from the data.
They do this by a process known as resampling.
Clay
Chia C Chong wrote:
Hi!!
Thanks for your reply...do you mean that Jackknife and Bootstrapping methods
area also some kind of goodness-of-fit
What is (are) the difference(s) between Statistics and Mathematical
Statistics?
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Only as an approximation.
At 12:57 PM 12/10/01 -0400, you wrote:
Art Kendall wrote:
(putting below the previous quotes for readability)
Gus Gassmann wrote:
Dennis Roberts wrote:
this is pure speculation ... i have yet to hear of any convincing case
where the variance is known
Mathematical statistics will require that you take 5, rather than 2, Advil
or Tylenol.
At 06:24 PM 12/10/2001 +, Andreas Karlsson wrote:
What is (are) the difference(s) between Statistics and Mathematical
Statistics?
=
At 03:42 PM 12/10/01 +, Jerry Dallal wrote:
Dennis Roberts wrote:
this is pure speculation ... i have yet to hear of any convincing case
where the variance is known but, the mean is not
A scale (weighing device) with known precision.
as far as i know ... knowing the precision is
In article [EMAIL PROTECTED],
kjetil halvorsen [EMAIL PROTECTED] wrote:
Slutsky's theorem says that if Xn -(D) X and Yn -(P) y0, y0 a
constant, then
Xn + Yn -(D) X+y0. It is easy to make a counterexample if both Xn and
Yn converges in distribution. Anybody have an counterexample when Yn
On Mon, 10 Dec 2001 12:57:29 -0400, Gus Gassmann
[EMAIL PROTECTED] wrote:
Art Kendall wrote:
(putting below the previous quotes for readability)
Gus Gassmann wrote:
Dennis Roberts wrote:
this is pure speculation ... i have yet to hear of any convincing case
where the
test please ignore
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Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
http://jse.stat.ncsu.edu/
The name given to the symbol @ in international standard character
sets is 'commercial at'.
See
http://www.quinion.com/words/articles/whereat.htm
for a history of the symbol.
Richard Wright
On Mon, 10 Dec 2001 23:34:19 +0100, Nathaniel [EMAIL PROTECTED]
wrote:
Hi,
Sorry for question, but
Title: Ç×°®µÄÅóÓÑ
Ç×°®µÄÅóÓÑ£º
atusually indicate some kind of rate or unit price 10 pounds @ $1
per pound
on the net is is used as a separator between the id of an individual and
his/her location
[EMAIL PROTECTED] id spoken as john dot smith at harvard dot e d u.
until the early-80's or so dot was spoken as point as
RE: The Poisson process and Lognormal action time.
This kind of problem arises a lot in the actuarial literature (a
process for the number of claims and a process for the claim size),
and the Poisson and the lognormal have been used in this context - it
might be worth your while to look there
besides, who needs those tables? we have computers now, don't we?
I was told that there were tables for logarithms once. I have not seen one
in my life. Is not it the same kind of stuff?
3. Outdated.
on the grounds that when sigma is unknown, the proper distribution is t
(unless N is
3) When n is greater than 30 and we do not know sigma, we must estimate
sigma using s so we really should be using t rather than z.
you are wrong. you use t-distribution not because you don't know sigma,
but because your statistic has EXACT t-distribution under certain
conditions. I know that
Sigma is hardly ever known, so you must use t. Then why not simply tell
the students: use the t table as far as it goes, (usually around
n=120), and after that, use the n=\infty line (which corresponds to the
normal distribution). Then there is no need for a rule for when to use
z, when to
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