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[Stepher]

Jim's Accusation

[kenbo]

Doing a google on Quatro Quatro brings up a page at OZemail.com.
on Suzie Quatro AKA Leather Tuscadero from happy days.

Into some wierd stuff there OZ?

[yorock]

Binary just to refers to probablities that have two outcome like flipping a coin..it can be either heads or tails. If you bet someone on the outcome of a coin toss you should win 50 percent of the time. Most casino games are more complex and all of them from a mathamatical standpoint have odds that favor the house. this casino is paying out more than it is taking in. That was evidently money they were unloading from the truck.
This supports someones theory that the entire operation is just an elaborate money laundering operation. (trade one dollar good money for $2 in bad money). While this certainly fits most of the facts so far I'm hoping for something more intersting

[Ozy_y2k]

Hey, I like PINKY Tuscadero, not LEATHER.

[mottjr]

The Binomial Theorem and Binomial Ditribution have many applications in the mathematical field of probability. Jim would have definitely made use of them in his calculations.

I could not hear the second part clearly. I seem to remember sort of disturbance happening at that point in the scene and his sentence sort of trailed off. I have never heard of "quatro quatro theory".

As far as explaining them to you though, that's best saved for another day.

[Diandra]

Mathematically Speaking

[Wouldn't u like 2 no?]

I believe a binomial is any term with 2 parts, for example, 3x. 3 and x would, separately, be monomials.

[LordKinbote]

Not quite. A binomial is a mathematical phrase with two separate terms, so 3x + 7 would be a binomial, its terms being 3x and 7.

---Scott

[cpababe]

And thus we are now discussing binomial equations rather than binomial distributions. Two different theorems. Check back a couple posts to get back on track.

[LordKinbote]

Yeah, I know, but I just had to correct the error.

This is what you want:

From http://www.ruf.rice.edu/~lane/hyperstat/A2301.html :

When a coin is flipped, the outcome is either a head or a tail; when a magician guesses the card selected from a deck, the magician can either be correct or incorrect; when a baby is born, the baby is either born in the month of March or is not. In each of these examples, an event has two mutually exclusive possible outcomes. For convenience, one of the outcomes can be labeled "success" and the other outcome "failure." If an event occurs N times (for example, a coin is flipped N times), then the binomial distribution can be used to determine the probability of obtaining exactly r successes in the N outcomes. The binomial probability for obtaining r successes in N trials is:

P(r) = (N! / (r!(N-r)!))(pi^r)((1-x)^(N-r))


where P(r) is the probability of exactly r successes, N is the number of events, and p is the probability of success on any one trial. This formula assumes that the events:
(a) are dichotomous (fall into only two categories)
(b) are mutually exclusive
(c) are independent and
(d) are randomly selected

[nevada1955]

Found this at mathworks.com. but the spelling is quantile-quantile. Could this be the plots Jim was using at the casino?



Quantile-Quantile Plots


A quantile-quantile plot is useful for determining whether two samples come from the same distribution (whether normally distributed or not).

The example shows a quantile-quantile plot of two samples from a Poisson distribution.

x = poissrnd(10,50,1);
y = poissrnd(5,100,1);
qqplot(x,y);



Even though the parameters and sample sizes are different, the straight line relationship shows that the two samples come from the same distribution.

Like the normal probability plot, the quantile-quantile plot has three graphical elements. The pluses are the quantiles of each sample. By default the number of pluses is the number of data values in the smaller sample. The solid line joins the 25th and 75th percentiles of the samples. The dashed line extends the solid line to the extent of the sample.

The example below shows what happens when the underlying distributions are not the same.

x = normrnd(5,1,100,1);
y = weibrnd(2,0.5,100,1);
qqplot(x,y);



These samples clearly are not from the same distribution.

It is incorrect to interpret a linear plot as a guarantee that the two samples come from the same distribution. But, for assessing the validity of a statistical procedure that depends on the two samples coming from the same distribution (e.g., ANOVA), a linear quantile-quantile plot should be sufficient.