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- Path: sparky!uunet!cs.utexas.edu!swrinde!mips!mips!munnari.oz.au!manuel!news
- From: Andrew.Robinson@anu.edu.au
- Newsgroups: sci.math.stat
- Subject: Re: Degrees of Freedom Was: Re: Standard Deviation.
- Message-ID: <1992Aug23.053852.19864@newshost.anu.edu.au>
- Date: 23 Aug 92 05:38:52 GMT
- References: <1992Aug14.172833.11844@cbfsb.cb.att.com> <c48nbgtf@csv.warwick.ac.uk> <WVENABLE.92Aug18180002@algona.stats.adelaide.edu.au> <1992Aug18.214711.6657@mailhost.ocs.mq.edu.au> <l95552INNa4h@roundup.crhc. <thompson.714506041@kiyotaki.econ.umn.edu>
- Sender: news@newshost.anu.edu.au
- Organization: Australian National University
- Lines: 66
-
- In article <thompson.714506041@kiyotaki.econ.umn.edu>
- thompson@atlas.socsci.umn.edu (T. Scott Thompson) writes:
- >Andrew.Robinson@anu.edu.au writes:
- >
- >>I'm going to go _way_ out on a limb here and say that I think that you have
- >>_one_ piece of infomation. If you don't have X then your data doesn't
- consist
- >>of a single number, it consists of a single observation from which you have
- >>measured a two variable vector - [Y,Z]. You know that there is an X and you
- >>want to use Y and Z to estimate it using a predetermined model, which you
- have
- >>specified. This seems to me to be a non-straightforward case of a
- multivariate
- >>regression, with two dependent variables and one observation.
- >
- >>Comments?
- >
- >Well first, I don't have to estimate X since I can calculate it
- >exactly from Y and Z.
-
- Not meaning to nit pick, but this is still estimation, as opposed to
- observation. Your estimation happens to be with a perfect model
- (congratulations).
-
- >Secondly, I agree that the single data point X
- >contains the exact same information as the vector (Y,Z). I will even
- >agree (although I am not sure that this is so obvious) that the number
- >of "pieces of information" in X (hence in (Y,Z) is one. After all,
- >the latter could simply be a normalization of units.
- >
- >The problem is to relate this to the usual degrees of freedom
- >accounting which seems to require that independent "pieces of
- >information" add up in some way. For example, we seem to think of Y
- >and Z as independent pieces of information because knowledge of one is
- >totally uninformative about the other. According to the adding up
- >rule, then, (Y,Z) should be 2 pieces of information, not one. Clearly
- >we agree that this is not so. The question is "why not?"
-
- I think I understand our difference. Correct me if I'm wrong, anyone, but
- degrees of freedom are used in the construction and validation of a model. The
- model presented by Scott here has already been constructed (and validated, I
- presume), therefore the degres of freedom would seem irrelevant to its further
- usage.
-
- Further to the idea expressed above, however, I think that the distinction in
- pieces of information lies in the difference between observations and
- variables. Each observation is a report, if you will, of how 'reality' (please
- let's not argue about this one :-)) is behaving at that point, irrespective of
- how many different independent variables are being measured. So when
- considering the number of independent 'pieces of information', we look at the
- number of observations with which we are constructing the model, not the number
- of variables for each observation. You seem to have one observation with two
- variables, ergo only one piece of information.
-
- >I think that the definition of "independent pieces of information"
- >that is implicitly used in the informal interpretation of "degrees of
- >freedom" does not satisfy an adding up rule, and thus fails to provide
- >a good interpretation for what "degrees of freedom" really represent.
- >Assuming they represent anything. I am skeptical about this.
-
- I'd be quoting previous posts if I continued on here.
-
- regardless,
- Andrew
-
- now hit d
-