This is more slowly, and more clearly as well. I hate to be the one to break
the news, but some time ago Man invented Mathematics to make these descriptions
even clearer.
It seems to me you are saying
prob(Y=1|Z) = .2(Z+2.5)
where Z is continuously distributed between -2.5 and + 2.5 (either as
A useful result is in the current Statistics in Medicine:
@Article{buy00r2,
author ={Buyse, Marc},
title ={$R^2$: {A} useful measure of model performance when
predicting a dichotomous outcome},
journal = SM,
year =2000,
volume = 19,
pages = {271-274
Rich Ulrich wrote in message ...
> ...could you explain the question more slowly?
Sorry for not giving enough motivation or context. Yes, I'm not comparing
the fit for continuous data with the fit for it's binary equivalent.
I have been thinking about the small values of R-squared values for
As most of the others, I am not sure what you mean (how the data are
generated). But if you do a regression with a binary Y, then actually
this is very much like doing a discriminant analysis or like computing
a t-test (if there is a single X). The R-sq is the ratio of between
variance to total va
On Sun, 6 Feb 2000, Milo Schield wrote:
> QUESTION: What is the theoretical maximum value of R-sq ** when binary
> data (Y) is obtained from a simple linear model?
Not clear what "obtained from a simple linear model" means. Are you
using a model to _generate_ values of Y? Or are you using s
On Sun, 06 Feb 2000 18:47:32 GMT, "Milo Schield"
<[EMAIL PROTECTED]> wrote:
> QUESTION: What is the theoretical maximum value of R-sq ** when binary data
> (Y) is obtained from a simple linear model?
>
> The data is binary with Y values taken from a linear model going from 0 to 1
> over the ran
QUESTION: What is the theoretical maximum value of R-sq ** when binary data
(Y) is obtained from a simple linear model?
The data is binary with Y values taken from a linear model going from 0 to 1
over the range of X.
The binary sequences of Y values are organized to minimize* the standard
devi
