I'm writing a simulator in C++. So far I have written a program to collect
data from a database and hope to be able to generate an algorithm to return
a random value with a distribution that matches my real world data. What
I'm finding is that the data is UGLY. In order to generate a reasonable
You only have four cells [(A,B) = {(0,0), (0,1), (1,0), (1,1)}], with
only 3 degrees of freedom among them. If you have three significant
effects (A, B, and A*B) your strategy in post hoc tests ought to be to
simplify the description, not to complicate things further. What you SAY
you expect
sounds as though you analyzed it wrong. did you treat it as a factorial
design?
I'd like to hear others' opinions regarding making the review process for
submitting papers to journals totally open. In my very limited experiences,
I've encountered referees who don't know what they're talking about. They even
make silly comments. IMHO, they would think twice before writing a
Diane,
Surely.
Take a real (finite size, say 1000) sample of product whose mean is 0,
standard deviation is 1, ~normally distributed, but discrete values from a
12-bit +-10volt AtoD (resolution = 10/(2^12)).
Kevin Hankins
[EMAIL PROTECTED]
-Original Message-
From: Bob Hayden <[EMAIL PR
That's how we get "self-fulfilling prophecies"! Actually, many companies are
so stingy with their IT investment that some kind of "scare scenario" may
have been necessary to get their attention. Seems a shame, but that's life in
the corporate world. Managers don't really take their computers serio
I am trying to figure out how to properly perform a "posthoc" analysis
of an experiment in which I exposed subjects to 2 different treatments
(drug A and drug B). Treatment A had 2 levels: treatment or no
treatment. Treatment B had 2 levels: treatment or no treatment.
We hypothesized that A is a t
I don't have a specific application to suggest, and you may already know
the paper I'm about to suggest, but for what it's worth, I highly
recommend the paper by Diaconis and Sturmfels, (1998) "Algebraic
Algorithms for Sampling from Conditional Distributions," Annals of
Statistics, v. 26, pp. 363
