Dice Probability: Calculating Consecutive Numbers

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The discussion focuses on calculating the probability of rolling consecutive numbers with three dice. The initial calculation yields a probability of 1/36, derived from the permutations of consecutive numbers and the total outcomes of 216. Participants confirm the calculation and discuss alternative methods, including a simulation using R to validate the theoretical probability. The simulation results in a probability of approximately 0.029, which closely aligns with the calculated value of 1/36. Overall, the calculations and simulations support the accuracy of the probability estimation for consecutive numbers on three dice.
boneill3
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Not sure if I should ask this here but

I'm trying to find the probabilty of consecutive numbers on tossing three dice. eg 1 2 3 , 4 5 6 etc

My workings so is

nPr = 3P3 (how many permutaions of 3 numbers in order)

= n!/(n-r)! = 3!/0! = 6

What
I did next is calulate all possible outcomes of three dice = 6 x 6 x 6 = 216

Therfore the probabilty of consecutive numbers on three dice = 6/216 = 1/36

Does this look right ?
I'm not quite sure if the permutaion calculation is right or if i have to multiply it by six...
regards
Brendan
 
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boneill3 said:
What
I did next is calulate all possible outcomes of three dice = 6 x 6 x 6 = 216

Therfore the probabilty of consecutive numbers on three dice = 6/216 = 1/36

Does this look right ?

Yep.

Going up: 3 ways of choosing the first die, other two are fixed: 3/216
Going down: 3 ways of choosing the first die, other two are fixed: 3/216
 
Thanks for your reply.
I've created an function using R to simulate rolling the dice 1000 times and I needed to compare it with the calculated probability.

Here' my code

RollDie=function(n) sample(1:6,n,replace=T) #function to roll 1 dice

result=0 #results hold the number of successive throws


for (x in c(1:1000) ) #loop 1000 times
{
die1 = RollDie(1) #roll first die

die2 = RollDie(1) #roll second die

die3 = RollDie(1) #roll third die


if (((die1 == die2-1) & (die2 == die3-1)) || ((die1 == die2+1) & (die2 == die3+1))) #if dice in consecutive order add to result
{
result = result+1

}
else
{}
}

print(result) #print number of successive values

P3succesivenumbers=(result/1000) #calculate probability

print(P3consecutivenumbers) #print probability



And the Probabilty came out as:

print(P3consecutivesivenumbers) #print probability
[1] 0.029


Which is pretty close to 1/36 = .0277

thanks
Brendan
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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