On 21 Aug 2001, Atul wrote:
> How do we calculate the adjusted r-square when the error degrees of
> freedom are zero ? (Or in other words, number of samples is equal to
> the number of regression terms including the constant.)
> Such a situation leads to a zero in the denominator in the expres
I have a doubt regarding adjusted r-square
How do we calculate the adjusted r-square when the error degrees of
freedom are zero ?
(or in other words, number of samples is equal to the number of
regression terms including the constant)
Such a situation leads to a zero in the denominator in the e
At 06:16 AM 8/21/01 -0700, RFerreira wrote:
>The formula wich gives the Standard Deviation ,
>SD=((x-mean)^2/(n-1))^0.5 ,can be applied to Any data set. When we
>have that value we know two things about the set: The Mean and the SD.
>With this two values We can have one powerful intuitive use to t
jim clark <[EMAIL PROTECTED]> wrote:
> "Chebyshev's Theorem: For any positive constant 'k', the
> probability that a random variable will take on a value within k
> standard deviations of the mean is at least 1 - 1/k2 ."
> This theorem holds for any distribution.
If you know that the distributi
Hi
On 21 Aug 2001, RFerreira wrote:
> The formula wich gives the Standard Deviation ,
> SD=((x-mean)^2/(n-1))^0.5 ,can be applied to Any data set. When we
> have that value we know two things about the set: The Mean and the SD.
> With this two values We can have one powerful intuitive use to the
On 21 Aug 2001, RFerreira wrote [edited]:
> The formula [for] the Standard Deviation, SD=((x-mean)^2/(n-1))^0.5,
> can be applied to any data set. [With] that value we know two things
> about the set: mean and SD. With these two values we can have one
> powerful intuitive use to them: The
Post-doctoral fellowship in biostatistical modeling
Department of Radiation Oncology
Mallinckrodt Institute of Radiology
Alvin J. Siteman Cancer Center
Washington University School of Medicine
Radiation Oncology has the aim of curing cancer by sterilizing tumor cells
using directed radiation beam
"Metric" as a noun is well established, though I sense that it's being
used in this way more frequently than before.
Check some dictionaries. A very good one online is
http://www.m-w.com/cgi-bin/dictionary , which gives 3 definitions for
the noun; the one you're referring to sounds like "2 : a
[EMAIL PROTECTED] (Dubinse) writes:
> I often see (and use) the term "metric" as a particular kind of
> measure. However, I have had difficulty in finding a clear definition.
> This is made more difficult because of the more common use of
> "metric" as an adjective denoting the system of measurem
The formula wich gives the Standard Deviation ,
SD=((x-mean)^2/(n-1))^0.5 ,can be applied to Any data set. When we
have that value we know two things about the set: The Mean and the SD.
With this two values We can have one powerful intuitive use to them:
The "centre" of the set is the mean and 68
[EMAIL PROTECTED] writes:
>
>I often see (and use) the term "metric" as a particular kind of
>measure. However, I have had difficulty in finding a clear definition.
>This is made more difficult because of the more common use of
>"metric" as an adjective denoting the system of measurement units.
>P
If you are drawing a venn diagram with only two circles of equal radius
overlapping, the distance between the centers is a direct (but thorny)
function of the common proportion you wish to display. You can come very
close with the following formula:
y = 3.5179*x^4 - 8.1729*x^3 + 6.8236*x^2 - 4
I often see (and use) the term "metric" as a particular kind of
measure. However, I have had difficulty in finding a clear definition.
This is made more difficult because of the more common use of
"metric" as an adjective denoting the system of measurement units.
Please tell me what I mean when I
Greetings..
Can anyone suggest me what are the differences between Box and Jenkin
Transfer function model and multiple regression model?
Are there any good tutorials or freewares that deal with the Box and
Jenkin Transfer function model?
Thanks
===
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