-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED]]On Behalf Of Wuzzy
Sent: Thursday, January 24, 2002 3:30 PM
To: [EMAIL PROTECTED]
Subject: Re: how to adjust for variables
I find it extremely difficult to interpret multivariate equations.
Are there any good books o
uniform distribution of what?
Unit Root testing theory uses asymptotic results, so underlying distribution
does not really matter as long as it satisfies some comditions.
Check out Davidson "Econometric Theory". You can find there a good intro
into unit roots tests. More advanced treatment is in
Neville X. Elliven wrote:
> >In Population 1, the chance of reaching 10 years of age is p=0.17, the
> >cance of reaching 13.5 is q=0.01
>
> I take this to mean:
> P{death before age 10} = 0.83
> P{death before age 13.5} = 0.99
Yup, that's what I meant
> It's a multinomial distribution, with t
> [ ... ]
> > Is doing a univariate regression between the variable you want to
> > adjust for and your predictor the only way to adjust for values as
>
> Univariate? Absolutely not. *Multiple* regression gives
> "partial regression coefficients." Those "adjust."
>
I find it extreme
Hi
On Thu, 24 Jan 2002, Rich Ulrich wrote:
> On 24 Jan 2002 07:09:23 -0800, [EMAIL PROTECTED] (Rich Einsporn)
> wrote:
> > Jim Clark gave a fine answer to the question posed by Sangdon Lee.
> > However, I am curious about the correlation and R-square figures given by
> > Sangdon. Apparently, th
On 24 Jan 2002 07:09:23 -0800, [EMAIL PROTECTED] (Rich Einsporn)
wrote:
> Jim Clark gave a fine answer to the question posed by Sangdon Lee.
> However, I am curious about the correlation and R-square figures given by
> Sangdon. Apparently, the R-squares for the simple linear regressions on
> X1
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There is an interesting article called "Sometimes R^2 > r^2_{yx1} +
r^2_{yx2}: Correlated Variables Are Not Always Redundant" by David
Hamilton in The American Statistician, May 1987, 41(2), pp. 129-134.
The paper gives an example in which there is little correlation between
y and either x1 or x2
Jim Clark gave a fine answer to the question posed by Sangdon Lee.
However, I am curious about the correlation and R-square figures given by
Sangdon. Apparently, the R-squares for the simple linear regressions on
X1 and X2 are (-.2)^2 = .04 and (.3)^2 = .09, but Sangdon says that the
R-sq for the
So, I was just wondering.you think he'd accept the $50 via PayPal??
reg
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED]]On Behalf Of Karl L. Wuensch
Sent: Wednesday, January 23, 2002 9:39 PM
To: [EMAIL PROTECTED]; [EMAIL PROTECTED]
Subject: Fw: I Hack Into Your
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