Statistics Canada is holding its 18th International Symposium on
Methodological Issues from October 16 to October 19, 2001, in Hull,
Quebec, just minutes away from Ottawa.
The theme of Symposium 2001 is "Achieving Data Quality in a Statistical
Agency: a Methodological Perspective". This symp
'Height of an individual' is only a random variable when the selection
of the individual and the measurement of his or her height is still in
the future. The full name of the variable is 'Height of a randomly
chosen individual'!
So if you take a random sample of adult males, each individual is
s
Philip Ackerman <[EMAIL PROTECTED]> wrote:
> Hello,
>I have a question regarding the term "independent, identically
> distributed" random variables. For example, suppose that your random
> variable is height, and the population of interest is adult males. You take
> a sample (size n) of adul
On 26 Jul 2001 09:29:52 -0700, [EMAIL PROTECTED] (Lise DeShea) wrote:
[ snip, previous]
> PCA explains variance. FA explains covariance.
If 'variance' of a variable is to be 1.0 using PCA, it is
(approximately) the multiple correlation using FA.
FA is not trying to fit the Unique variance
[EMAIL PROTECTED] (Eric Bohlman) wrote in message
news:<9jo444$jph$[EMAIL PROTECTED]>...
Thank you for mentioning this. This is a problem for statisticians
generally, not just those who work with Likert-type or similar items.
A very important example concerns drug response in pharmaceutical
cl
The common factor model is compatible with the idea that you have
unobserved constructs that you wish to estimate using item responses.
The constructs are presumed measured with error. A common factor
model takes this error into account, whereas PCA does not.
When we're talking about multiple p
At 09:27 AM 7/26/01 -0700, Philip Ackerman wrote:
> Hello,
>
>What I do not understand, is why >>> individuals' heights<<< is a
>random variable
instead of individual' s heights ...
if you change the wording abit above ... it might make more sense
>_
Hello,
I have a question regarding the term "independent, identically
distributed" random variables. For example, suppose that your random
variable is height, and the population of interest is adult males. You take
a sample (size n) of adult males, and measure the variable for each subject.
T
Magenta wrote:
> Why a factor analysis and not a principal components analysis? I've been
> taught that a principal components analysis makes fewer assumptions on the
> data, so
> assuming that one can perform a factor analysis then automatically one can
> also perform a principal components ana
Rich Ulrich is correct that a dichotomy is interval, at least as far as
what arithmetic it is permissible to perform on it.
It is not so clear that it is interval in terms of what operations are
NOT trivial to perform on it. If I have numerically-coded ordinal data
I may recode using th
On 25 Jul 2001 12:33:49 -0700, [EMAIL PROTECTED] (Dennis Roberts) wrote:
> inherent problems related to LICKert items and level of measurement that
> create problems would be these too
>
> 1. how many response categories are there for AN item??? by the way ...
> likert used many types ... incl
Dear colleagues,
I applied correspondence analysis on a frequency table (16x22) in
which some of the observed cells have zero frequencies. I was able to
find out patterns very well, showing horseshoe effects. Since the
correspondence analysis uses chi-square value, I heard it is necessary
that
You definitely should drop the idea that the seasonla factors are neutral
(sum or product of them is equal to unity) when working with MOVING
seasonality (thsi can be simply demonstrated). The only case where it is
true is the regression approach (with dummy variables). I advise you to try
the Cen
At 12:51 PM 7/26/01 +0200, you wrote:
>Hi,
>
>For some stats explaining I need an excel sheet that calculates and graphs
>the normal(Bell)-shaped curve of some data set with a given average and a
>known stdev.
>It would be nice to demonstrate the calculation the chance of occurance of
>some given
>
>1) Responses in the middle of the scale represent something
>*qualitatively* different from responses near the ends. For example, if a
>particular issue isn't relevant or applicable to some of the
>subjects, they're likely to respond in the middle, but this doesn't mean
>the same thing as som
Magenta wrote:
>
> "Robert J. MacG. Dawson" <[EMAIL PROTECTED]> wrote in message
> [EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
> > Can you do factor ananlysis, then? Probably yes.
...
>
> Why a factor analysis and not a principal components analysis?
Dunno. But the interesting part of t
Hi,
For some stats explaining I need an excel sheet that calculates and graphs
the normal(Bell)-shaped curve of some data set with a given average and a
known stdev.
It would be nice to demonstrate the calculation the chance of occurance of
some given value in relation to the data set.
Does any
Hi all !
Our research team is developing software for processing of X-Ray images
(gray images), obtained by examination of large-scale objects using a
high-energy electron accelerator. The main task of our software is to reveal
the irregularity, cracks, inclusion and other defects in the object.
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