on a random string of digits number 123 appears every 1000 digits, 221 every995digits
 

i learnt small basic to study probability in order to find a way to beat the casino and i made this interesting disocovery

if you take a long string of decimal numbers the string 123 will apeear every 1000 digits while the 221 every 995 digits

this small basic program proves it



start:
TextWindow.WriteLine( digitcount/twotwoone)
a= Math.GetRandomNumber(10)
digitcount= digitcount+1
If a=2 Then
Goto seconddigit
Else
Goto start
EndIf
seconddigit:
a= Math.GetRandomNumber(10)
digitcount= digitcount+1
If a=2 Then
Goto thirddigit
Else
Goto start
EndIf
thirddigit:
a= Math.GetRandomNumber(10)
digitcount= digitcount+1
If a=2 Then
Goto thirddigit
EndIf
If a=1 Then
Goto bingo
EndIf
If a<>1 And a<>2 Then
Goto start
EndIf
bingo:
twotwoone= twotwoone+1
Goto start


so for example in the roulete is much more likely to appear the combination 2nd,2nd,1st column than 1st,2nd,3rd column

i love these kind of problems either they keep you entertained for the rest of your life or hav a huge victory :)

Selecting random digits from 0 to 9 inclusive, the probability of getting a 1-2-3 combination (in that order) is: 1/10 x 1/10 x 1/10 = 1/1000

Selecting random digits from 0 to 9 inclusive, the probability of getting a 2-2-1 combination (in that order) is: 1/10 x 1/10 x 1/10 = 1/1000

Your statement that "so for example in the roulete is much more likely to appear the combination 2nd,2nd,1st column than 1st,2nd,3rd column", is incorrect for 2 reasons:
1. Firsly, it is not correct to say that a probability of 1/995 is 'much more likely' than a probability of 1/1000. Statistically speaking these probability values are virtually identical.
2. Your result of a probability of 1/995 for a number string of 2-2-1 being different from 1/1000 for number string 1-2-3 simply represents the natural randomness of your basic computer random function, and not a true statistical difference in probability. Simple probabilty theory (above) shows that the probabilty of getting a 1-2-3 is the same as the probability of getting a 2-2-1.

Run your program again & again. You will get probabilty values for 2-2-1 combination very close to (but not not always exactly) 1/1000.

probability of getting 123 or 221 is equal IN A 3 DIGIT STRING

probability of getting 221 is bigger than getting 123 in A MILLION DIGIT STRING

this is old and not really related but here as you can veryfy with your browser searcher number 21 appears(with the searcher rules) 10% more than number 11

check yourself dont just believe me

http://www.eveandersson.com/pi/digits/1000000

even if you were right, it's a 5‰ difference, got luck exploiting it.

oh i dont want no money just to prove eisntein wrong whom i dislike for being a psicological mistreateer

in fact quantum phisics expect a certain degree of nonlocality in cahnce as theres in space

oh and thats a 0.5% difference

what you think the symbol "‰" stands for? :rolleyes:

and why einstein?

oh i usually take accepted as imposible problems to solve for the fun

both my folks are mathematicians so have it on the genes

i dont know i dont see eisntein as a role model, succesfull people pass to history as role models while they trully were sick persons

another example of this is picasso a socialized psicho who influenced several women into suicide by making magic with art(if you rather a more rational aproach that is depicting them in a grotesque manner)

There are problems with your methodology. The datatset you are using (1 Million Digits of Pi) is NOT random for your purposes. May I suggest you download a randomly generated dataset from somewhere like www.random.org or find other sources of random data and re-run your analysis. I assure you, that p(1,2,3) = p(2,2,1) = 1/1000

i dont think chance is random either just unknown

you dont get the pi thing which makes 21 appear a 10% more than 11, a clue:

number 21112 is two 11 or one 11?

but the thing is that this is not appliable to 221 vs 123

I agree that the digits of pi throw up some rather interesting combinations, but you just cannot use these digit combinations to make a statements about probability of digit combinations from a random data set or a gambling table.

Your statements about statistical likelihood assumes that each trial (the selection of the next digit) is indepenent of all other trials, but this is not the case. The next trial (next digit selection) is dependent upon the previous digit in the sequence of Pi since any repention of Pi the nth decimal place is fixed and repeatable. I's be interested to see your results once you've repeated your analysis with a different (random) dataset.

well that problem is also present in my program since it uses a pseudo random secuence just as pi

but that would be exactly the case in an online casino

for what i know an online casino could be using a pi secuence after the 1 billionth digit

and were getting in enjoiable crazy stuff here:

you pass a beam of light trough a double slit

you put no detector light shaps a wave pattern in the photosensible surface

you put a detector in the RIGHT slit

light will shape a paricle in the surface

but how does it know when it goes through the LEFT slit?

answer:

when you dont pay attention to me i dont exist, im a product of your mind

i say it this way because if i applied to me you would think me crazy :)

Actually you are both wrong.

For example, given a set of real numbers {1,2,3 ... 10000}

The string 123 occurs 11 times {123, 1123, 1230, 2123, 3123, 4123, 5123, 6123, 7123, 8123, 9123} as does the string 223 {223, 1223, 2223, 2230, 3223, 4223, 5223, 6223, 7223, 8223, 9223}.

Thus the probability is 1:1000 for 1000 real numbers but 11:10,000 for 10,000 real numbers.

The probability changes with sample size, getting larger as the set of reals being sampled gets larger.

You need to work out probabilities for a population rather than a sample.

You can of course manipulate the sample size to get aberrant results. For example the set of reals {1,2,3 ... 1200} has two elements containing the string 123 and only one element containing the string 223. This results from ad hoc manipulation of set size to get the results you want.

We should leave that sort of non-scientific ad hoc data manipulation to conspiracy theorists and climate change deniers :D

well statistics are pretty crazy i found the flaw

i left the computer just half hour calculating, when i returned from a coffe in the bar i assumed it would be the right result but leaving it 3 hours gave out number 221 appears every 1000 digits as it should

hell computers are stone age, so slow

Correction. Your set {1,2,3 ... 10000} is actually a finite subset of Natural Numbers. Moreover, your subset excludes zero, but your derivations include zero??? Keep drinking the Kool-Aid son of Al Gore:-P. Btw, it has recently been reported that the manpig got a D in the natural sciences class!:grin:

edit:
Well, I'll cut some slack about the zero. But raaaids function a= Math.GetRandomNumber(10) does it generate 0-9 or 1-10? I'm assuming 0-9.

true the sample sets should be {0,1,2,3 ... 1199} etc

That doesn't change the fact that probabilities change with sample size.

http://www.nizkor.org/features/falla...d-hominem.html :D

if you have an ugly lottery number like 1444 its as likely to turn out as 2643 if you count the number 4444 as two 444 but less likely if you count 4444 as just one 444

now if common folk call it an ugly lottery number who knows