Mathematics and the Lottery

I really like this burstiness concept...and is something that i have tracked to get wins...

as i sit here in front of my excel spreadsheet i am trying to anticipate what numbers will burst, and why?

What groups will burst...how do burst look and what are the best bursts...

I am not a programmer so i do most of my analysis the old fashioned way...

i understand filters to a point...but the best filter is my brain.

I have simplified my pick 3 and pick 4 approach to anticipating repeaters as the game can be daunting if you try too much...

any way JKing...good luck.  Recently in pick 4...three of the four numbers repeated...in a burst....

burstiness happens in a approximate cycle of 36 games...

Hello, boner lotto, a lottery for 39/5 matrix in the previous post is 100% in any lottery  Catch always 1st step to separate the first digit 1-3 and the last digit 0-9  These formations from 0000 to 3333 can take all such pairs = 0022 or all odd 1113, taking it further diminishes the 5th random number is 30 to 39 the number 56  Example 38 may give evidence with which training from 0000 to 3333 is not out yet But lottoboner the 37 groups are 100%, but attention is half the sitema, missing endings ok

Hello lootoboner, fantasy 5 california test = 6823 = 15 24 27 29 31  5 digit number the first 1_2_2_2_ leatorio 30-39 gave 31 1,2,2,2 Lottoboner these are within the 37 groups because it is 100%  Sure miss the last digit, 5,4,7,9 5th number is random, does not enter the study  In Example 31, the biggest problem is terminaçaoes, but always one or two repeats  Endings of the previous test. then we have the 37 groups of 27 or 31 to 39  The accuracy is 100%, but only four endings that you can use like a pick 4  That forum has a lot of filter systems and implement

so what is your interesting bursty structure?

Hello, lottoner, you have to use 37 as a fixed or reference, ode to choose, as having frequencies of 37   Average hot and cold, to reduce, or example the number 38 = see which formations   From 0000 to 3333 who had not left with the number 38, to reduce even more the terminations   You can play all pairs = 2,2,4,8 or all odd = 1137, the 5th random number is ok, the four endings, you can see by the statistics in each vertical position, or as a pick4! Of the 37 that are 100% not escape, are perfect because there is only one formation per drawing. So practically 5 fantansy was like a pick 4

when i say structure i am referring to the pictures of which the algorithm would apply...this is from the video posted plus the bursty nature...

are you saying all odd forms some sort of interesting structure?

LB

I got a little off topic and the logic-gates are not part of the bursty patterns. 

Prediction is not one of my strong points and I am not sure if it can be done

in any manner that will consitantly hit.  Even a broken clock is correct twice

each day meaning that even if something shows good it may be just chance

unless it can be repeated many times within a short time frame.   I believe 

that if using probability to come to some sort of prediction then your results

will fall very close to the expected.   In many of the analysis that I run I find

clusters of hits and misses and from this data I find that I can recognise when

a data will start a cluster and approxmently when it will end.  This does not

mean that I can make an exact prediction but rather I know when to start

playing some value.  The problem is finding  enough clusters from different

data sets that all converge at or near the same time.  Bursty analysis may prove

very useful in this but will require much work to find a solution.

RL

I apologize to all players regarding my last post...

I did not specify the game i am playing...

but i posted to show examples of "burstiness"...the flag formation could be a bursty behaviour....

All pink highlights are hot as in hot pink formatting...

the chart is a Matrix Walk aka DSH...

i forget which game...either 5/39 or 6/59, i also forgot the date, although the chart indicates the date...

Bursty!!!!!!!

Considering the main issues of the regression models in the econometric analysis,
in practice, the researcher often faced with a situation where the regression is given a "bad", ie, t-statistics, most estimates are small, reflecting the insignificance of relevant independent variables. At the same time, the F statistic can be quite large, which indicates the significance of the regression as a whole. One possible reason for this phenomenon is known as multicollinearity and occurs when there is a high correlation between the factors.
One of the conditions of the classical regression model is the assumption of linear independence of the explanatory variables. When that condition, ie when one of the variables is a linear combination of the others, is called a complete collinearity. In this situation, you can not use ordinary least squares (OLS). In practice, the complete collinearity occurs very rarely. Much more often faced with a situation where among the factors that there is a high correlation. Then indicate the presence of multicollinearity. In this case, the least squares estimation (least squares method) formally exists but has a "bad" properties.
Multicollinearity can occur due to various reasons. For example, several independent variable may have a tendency common time, which undergo small oscillations. In particular, this can happen when the value of an independent variable values are dating each other.
only some of the most characteristic signs of multicollinearity.
One. A small change in the source data (such as the addition of new observations) leads to a significant change in the estimates of the model.
2nd. The estimates have large standard errors, low value, while the overall model is significant (high determination coefficient R2 and the corresponding F-statistics).
3rd. Coefficient estimates are incorrect from the point of view of the theory of signs or oversized.
do you do when all signs that there multicollinearity? The answer to this question is no, and among econometricians have different opinions about it. When confronted with the problem of multicollinearity can be a natural desire to discard the "extras" independent variables, which can serve as its cause. However, be aware that it can cause new problems. First, it is not always clear which variables are redundant in this sense. Multicollinearity means only approximately linear relationship between the factors, but not always allocate the "extra" variables. Secondly, in many cases, removal of all independent variables can have a significant impact on the essence of the model. Finally, the rejection of variables called essential, i.e., truly independent variables that affect the dependent variable studied, resulting in a change of the model. In practice, usually when a multicollinearity factor cleaned at least for the analysis and then repeat the calculations

Fyi,

  Programming languages for free.

http://numpy.scipy.org/

http://code.google.com/p/mpmath/

Simple math, is all that is needed.