Re: Looking for feedback for an online multivariate regression tool...


Richard Ulrich wrote:
On 29 Nov 2006 09:33:57 -0800, "Reef Fish"
<large_nassua_grouper@xxxxxxxxx> wrote:
[snip]

that's like Richard Ulrich calling Y = bX a nonlinear regression model.
:-)

Misrepresent. In irrelevant thread. Slur. More fair to
mention, "constraints". Mention, "proposed".

Misquote! misrepresent. Ignored smiley in the ANALOGY humor.

I was commenting on the OP's misuse of "multivariate regression":

RF> it had "multiple regression" which is NOT "multivariate
regression", so
RF> that's like Richard Ulrich calling Y = bX a nonlinear regression
model.
:-)

Richard Ulrich was mentioned in the analogy because Richard Ulrich
ALSO didn't know that a "multivariate regression" is NOT a "multiple
regression", which is a "unvariate multiple regression". Richard
argued
and argued. Here is the thread:

http://groups.google.com/group/sci.stat.math/msg/f3f6d4e3cfda69c1

Actually Google found 61 threads with Ulrich and "multivariate
regression"
in which he made the same error of misidentification of what a
multivariate regression is: One with MORE THAN ONE dependent
variables for the same set of independent variables.


So, in this case, Richard Ulrich made TWO errors and I used the
analogy to point out the OP's error in the use of "multivariate
regression" as being ALMOST as bad as Richard's Y = bX
calling it a nonlinear regression!

See, it's not at all like the way Richard misquote and TWIST his
defense into an attack.


I'm thinking -- Does Reef Fish feel tremendously
inferior, that he has to attack me so promiscuously?

I attack your statistical ERRORS -- it's unfortunate that Richard
Ulrich is the person who made those errors isn't it?

(and attack other folks, ditto?)

Yes, I attack the ERRORS of the other folks just the same.


As to the above. Long ago,
Bob refused to consider alternate terminology for
teaching linear models. *He* thinks it's a big deal.

So, right here in one post, Richard Ulrich through his own
NOISE of attacking me, revealed his own errors in:

1. the term "multivariate regression"
2. his errors in the standard meaning of "linear model" in statistics
3. the WORST of his errors, calling Y = bX a "nonlinear model"

and he whines that I feel "tremendously inferior" to need to
attack him?

That's just the same Richard Ulrich, the Chief Quack in rec.stat.math
and all THREE groups. No one else comes close to him in terms
of making STATISTICAL ERRORS.

And every time he make posts like this, he merely sticks his own
foot further up his mouth to show everyone what his errors were.

-- Reef Fish Bob.

.

Relevant Pages

  • Re: Scott Seidmans PURE NOISE on: "Non-linear regression problem"
    ... Scott Seidman wrote: ... the substantive subject of nonlinear regression estimation is ... Richard Ulrich argued the same, ... Statistics in 1938, long before I had used it, and he used it EXACTLY ...
    (sci.stat.math)
  • Re: Linearity- Multiple Regrression
    ... Richard Ulrich gave you some erroneous advice about ... >> what to look at in the scatter plots. ... Just exactly what RELEVANCE that has to the multiple linear regression ... >> Multiple Regression Analysis", for the reasons I'll explain below. ...
    (sci.stat.math)
  • Re: Rich Ulrich continues his statistical Muddle, Quackery, and MALPRACTICE
    ... >>> muddle about the standard regression assumptions, ... A discrete uniform distribution of ranks. ... why was sehwail and Richard Ulrich want to check the "normality" ...
    (sci.stat.math)
  • Re: Richard Ulrich used THREE posts to distort one post in one thread
    ... That was when Richard Ulrich took it as his opening to push his ... Quackery in regression analysis. ... Those are patterns with Bob that I've noted several times. ... Linear regression might be the least ...
    (sci.stat.math)
  • Re: Rolling up variance
    ... Jack Tomsky on his succinct admission of error in which I concluded: ... Richard Ulrich butted in with his gratuitous attack. ... DEFINITION of "Multivariate Regression" and its statistical substance. ...
    (sci.stat.math)