Bidirectional Mean Averaging and The Wave Matrix

Reply #28

Hyperdimension — Sun, 13 May 2007 05:18:53 GMT

Thank's JadeLottery,

I'll continue analyzing more of The Quantum Lottery Theory, it is very interesting..

Reply #27

JADELottery — Sun, 13 May 2007 05:08:26 GMT

PD: Are there any excel available for Bidirectional Mean Averaging and The Wave Matrix?

The Wave Matrix is too Dynamic and too involved to put in an Excel sheet.

I use Visual Basic to derive The Wave Matrix and The Wave Matrix Quantum Projections.

Also, this only one part of many in The Quantum Lottery Theory I'm working on to Select Numbers.

Reply #26

JADELottery — Sun, 13 May 2007 05:02:41 GMT

It's up and coming... but if you read carefully, you might see what I'm saying. Some of it is embedded in the postings.

Reply #25

Hyperdimension — Sun, 13 May 2007 04:56:34 GMT

Hi JadeLottery,

What means a quantum fog of data or a quantum fog to the combinatorial set?

Thank you..

PD: Are there any excel available for Bidirectional Mean Averaging and The Wave Matrix

Reply #24

JADELottery — Fri, 11 May 2007 02:30:20 GMT

Yeah, if there is going to be something like a Heisenberg Compensator like in Star Trek, it's probably going to have Bidirectional Mean Averaging and the Wave Matrix in it somewhere or some derivation thereof.

Reply #23

WSN1 — Fri, 11 May 2007 02:21:45 GMT

Great Work JadeLottery...This is very interesting

Reply #22

JADELottery — Thu, 10 May 2007 19:43:38 GMT

Been a little side tracked with Chaos-Order Duality, but still on my mind.

Continues...

Reply #21

JADELottery — Wed, 02 May 2007 13:59:43 GMT

Below is a basic example of the difference between Classical and Quantum. The example shows some data points and the Classical average line in blue and a statistical range from the average in red. The lines are fixed and encompass most of the data. It also shows the data and Classical superimposed on each other. The Quantum version of the data is a blurry circle, no real definable edge, but the overall image gives an idea of what the shape of the data is.

Data Points

Classical... [ More ]

Reply #20

JADELottery — Wed, 02 May 2007 13:03:42 GMT

In Quantum Projection, the same mean and standard deviation are used, however, the projections are not fixed. The projected mean and projected ranges are allowed to fluctuate randomly based on the standard deviation from the mean. Also, the data that is produced is given a little Quantum Jitter. Overall, the effect of Quantum Projection creates a kind of Quantum Fog of Data. Even though the mean and ranges are not clearly defined, there is enough there to get a sense of the data is as a whole.

Reply #19

JADELottery — Wed, 02 May 2007 12:53:10 GMT

This will be fairly generalized, as there are many different statistical analysis and many different formulae that this could apply to. I will refer to the Normal Distribution as a guide; this is to give some kind of reference point to relate to.

In Classical Projection, a mean and standard deviation can be derived to produce a Normal Distribution that can show where the concentration data has occurred relative to the mean or average data point. Typically, you would say the data will fall on

Reply #18

JADELottery — Wed, 02 May 2007 12:35:18 GMT

Alright, let's try and finish this one up. I have ten different projects going on and very little time to finish. Anyway, next on the list is the difference between Classical Projection and Quantum Projection. Each uses the same data, however, how you arrive at a projection is slightly different than what most people are use to.

continues...

Reply #17

JADELottery — Sun, 08 Apr 2007 01:34:13 GMT

The Wave Projection Elliptic Field Equations show promise, but needs more work than I have time for currently. There are a few Field Vectors that seem to have cropped up in the Transforms. Looks like they are linear relative to the closed loop elliptic and parallel to one of the axis, but could be something else. I'll keep it as a side project.

continues....

Reply #16

JADELottery — Sat, 07 Apr 2007 08:48:11 GMT

Things might change a bit. I've been working on a Wave Projection Field Equation(s) that might take this in a slightly different direction. However, I'll still work it using some of the Wave Projection Equations I already have. I'll get back in a while.

continues...

Reply #15

JADELottery — Mon, 02 Apr 2007 21:20:03 GMT

Low Frequency Wave Data can also utilize Random Number Transforms - Normal Distribution to approximate a projection. Below is an example of a few projections and the relationship to the Wave Data (W1). It shows that it is less likely the line will project far up or down from the curve/line and is more likely to project near the last calculated value.

It will be from the analysis of the Wave Data (W1) that will determine the Standard Deviation for the projection.

continues...

Reply #14

JADELottery — Sun, 01 Apr 2007 23:14:21 GMT

We use the Wave Matrix and Remainder data to project the deviation from the Regression component. How we handle each Wave Data and Remainder Data is dependent on the frequency of each component. Wave Data (W1) is a relatively low frequency and can be projected to a value that is close to the last Indexed value. Wave Data (W5) and the Remainder (R5) are relatively high frequencies and can be approximated using a Normal Distribution calculation that has the average centered on the X-axis and the S... [ More ]

Reply #13

JADELottery — Sun, 01 Apr 2007 22:04:54 GMT

In making a projection, we need to understand each component. The Regression is the easiest, just extend the Index to the next number in its sequence. For the example I've provided, the next Index value would be 179 and the Linear Regression value would be 1,524.61. Next the Wave Matrix and Remainder Data need to be projected to the next Index value. The Wave Matrix and Regression Data are derived from the Oscillation Data and approximate the individual Wave Components of the Oscillation Data.... [ More ]

Reply #12

JADELottery — Sun, 01 Apr 2007 21:03:33 GMT

As an example, we can look at the first few rows of the table I posted a while ago.

Index

n

Ball #01

Dn - Draw

Occurrence

Linear Regression

Ln = m * n + b

m = 8.29, b =40.70

Oscillation

Data - A

{Dn - Ln}

Wave Data - W1

d1 = -0.87

i = 32

Wave Data - W2

d2 = 0.73

i = 32

Wave Data - W3

d3 = 1.89

i = 32

Wave Data - W4

d4 = 3.06

i = 32

Wave Data - W5

d5 = 3.83

i = 32

Remainder - R5

1

16

48.98

-32.98

Reply #11

JADELottery — Sun, 01 Apr 2007 20:32:54 GMT

Alright, let's take this in a few steps. First, the components of the projection are the Regression, Wave Matrix and Remainder values. Each of these, when summed together, equal the Observed data. The Observed data in this case is the Draw Occurrence. However, the Observed data is not restricted to just Draw Occurrence. The Observed data could be the Number drawn in a specific column, the Sum of the Numbers in each column or any data where a Regression curve/line can be derived to create an Osci... [ More ]

Reply #10

JADELottery — Tue, 27 Mar 2007 09:10:48 GMT

I've been setting this aside too long, other work and projects gobbling up my time.... this week, this week... get a move on.

Reply #9

JADELottery — Tue, 13 Mar 2007 21:51:01 GMT

Whoo, another one.... the storm ain't over yet.

Hang on boys and girls, we're going for a ride!!!

Yee-Haw....

Reply #8

JADELottery — Tue, 27 Feb 2007 21:57:59 GMT

Whoo, I'm glad I used the Wave Matrix to analyze my 401k and put all my money into Cash back in Mid Dec.

Now the fun part, make even more money after the Market gose Lower.... YEAH!!!

I love those Waves and the Matrix it's in.

Reply #7

JADELottery — Thu, 15 Feb 2007 21:06:15 GMT

it does seem that way, but in the end it will be simple like a computer that can fit inside a single room.

if learning the knowable were easy, everyone would be a genius.

Reply #6

pumpi76 — Tue, 13 Feb 2007 07:18:40 GMT

JadeLottery, God knows i'll like to follow you but is all too complex for me...

Reply #5

JADELottery — Wed, 24 Jan 2007 22:11:42 GMT

A few side Tracs... but still working on it.

Reply #4

JADELottery — Sat, 30 Dec 2006 09:20:32 GMT

The Wave Matrix has been on hold for a bit while I format H-Trac for Pick 3 and Pick 4.

I'll update this thread when I have time.

Thank you for you patients..

Reply #3

JADELottery — Sun, 10 Dec 2006 13:38:13 GMT

I'm tired, I'll work on Wave Matrix projection and probability projection at another time.

I'll pop in now and then.

For now, .

Reply #2

JADELottery — Sun, 10 Dec 2006 12:47:25 GMT

Here are a few graphs of the table

Reply #1

JADELottery — Sun, 10 Dec 2006 12:46:16 GMT

Here's a table of Draw Occurrence, Linear Regression of Occurrence, Oscillation Data, Wave Matrix, and Remainder Data for WI Lottery of Ball #1 for 1500 Draws:

Index

n

Ball #01

Dn - Draw

Occurrence

Linear Regression

Ln = m * n + b

m = 8.29, b =40.70

Oscillation

Data - A

{Dn - Ln}

Wave Data - W1

d1 = -0.87

i = 32

Wave Data - W2

d2 = 0.73

i = 32

Wave Data - W3

d3 = 1.89

i = 32

Wave Data - W4

d4 = 3.06

i = 32

Wave Data... [ More ]

Bidirectional Mean Averaging and The Wave Matrix

JADELottery — Sun, 10 Dec 2006 12:45:09 GMT

Bidirectional Mean Averaging and The Wave Matrix

1 - Oscillation Data Set

A = {X1, X2, X3, ... , Xn-2, Xn-1, Xn}

A - oscillation data set is any set of X values oscillating on the x-axis.

oscillating X values can be obtained by removing any regression components.

possible regression components are:

constant, linear, exponential, logarithmic, power, geometric, polynomial, etc.

2 - Up Mean Averaging

U1 = X1... [ More ]