On 28 Feb 2002 07:37:16 -0800, [EMAIL PROTECTED] (Brad Anderson)
wrote:
> Rich Ulrich <[EMAIL PROTECTED]> wrote in message
>news:<[EMAIL PROTECTED]>...
> > On 27 Feb 2002 11:59:53 -0800, [EMAIL PROTECTED] (Brad Anderson)
> > wrote:
BA > > >
> > > I have a continuous response variable that range
On 27 Feb 2002 17:16:26 -0800, [EMAIL PROTECTED] (Dennis Roberts) wrote:
> i thought of a related data situation ...but at the opposite end
> what if you were interested in the relationship between the time it takes
> students to take a test AND their test score
>
> so, you have maybe 35 studen
[EMAIL PROTECTED] (Eric Bohlman) wrote in message
news:...
> Rolf Dalin <[EMAIL PROTECTED]> wrote:
>
> IIRC, your example is exactly the sort of situation for which Tobit
> modelling was invented.
Considered that (actually estimated a couple of Tobit models and if I
use a log transformed or bo
Rolf Dalin <[EMAIL PROTECTED]> wrote:
> Brad Anderson wrote:
>> I have a continuous response variable that ranges from 0 to 750. I only
>> have 90 observations and 26 are at the lower limit of 0,
> What if you treated the information collected by that variable as really
> two variables, one ca
On 27 Feb 2002 14:14:44 -0800, [EMAIL PROTECTED] (Dennis Roberts) wrote:
> At 04:11 PM 2/27/02 -0500, Rich Ulrich wrote:
>
> >Categorizing the values into a few categories labeled,
> >"none, almost none, " is one way to convert your scores.
> >If those labels do make sense.
> well, if 750
At 07:37 AM 2/28/02 -0800, Brad Anderson wrote:
>I think a lot of folks just run standard analyses or arbitrarily apply
>some "normalizing" transformation because that's whats done in their
>field. Then report the results without really examining the
>underlying distributions. I'm curious how f
Rich Ulrich <[EMAIL PROTECTED]> wrote in message
news:<[EMAIL PROTECTED]>...
> On 27 Feb 2002 11:59:53 -0800, [EMAIL PROTECTED] (Brad Anderson)
> wrote:
>
> > I have a continuous response variable that ranges from 0 to 750. I
> > only have 90 observations and 26 are at the lower limit of 0, whi
Brad Anderson wrote:
> I have a continuous response variable that ranges from 0 to 750. I only
> have 90 observations and 26 are at the lower limit of 0,
What if you treated the information collected by that variable as really
two variables, one categorical variable indicating zero or non-zero
i thought of a related data situation ...but at the opposite end
what if you were interested in the relationship between the time it takes
students to take a test AND their test score
so, you have maybe 35 students in your 1 hour class that starts at 9AM ...
you decide to note (by your watch) t
Brad Anderson wrote:
>
> I have a continuous response variable that ranges from 0 to 750. I
> only have 90 observations and 26 are at the lower limit of 0, which is
> the modal category.
If it's continuous, it can't really have categories (apart from those
induced by recording the variable to
At 04:11 PM 2/27/02 -0500, Rich Ulrich wrote:
>Categorizing the values into a few categories labeled,
>"none, almost none, " is one way to convert your scores.
>If those labels do make sense.
well, if 750 has the same numerical sort of meaning as 0 (unit wise) ... in
terms of what is being
On 27 Feb 2002 11:59:53 -0800, [EMAIL PROTECTED] (Brad Anderson)
wrote:
> I have a continuous response variable that ranges from 0 to 750. I
> only have 90 observations and 26 are at the lower limit of 0, which is
> the modal category. The mean is about 60 and the median is 3; the
> distributio
I have a continuous response variable that ranges from 0 to 750. I
only have 90 observations and 26 are at the lower limit of 0, which is
the modal category. The mean is about 60 and the median is 3; the
distribution is highly skewed, extremely kurtotic, etc. Obviously,
none of the power transf
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