Consecutive Heads/Tails in weighted coin toss
Hi,
Can someone please help with this. I keep getting different answers to this from different people.
Supposing you have a weighted coin which lands heads up 70% of the time and tails up 30% of the time.
Q. What is the probability that it lands on tails 20 or more times in a row if I did this 1000 times?
*Also could I have the equation on how you worked this out so that I can change the weighting, number of trials etc (I need to know this for my business!!!)
Thank you very much.
Zak
Re: Consecutive Heads/Tails in weighted coin toss
Hi, great answer from awkward. But how did you figure out the expected number of runs of R or more successive tails:
\lambda = p^R \,[(N - R) q + 1].
You derived this formula from scratch. I tried to figure by myself this result and tried to Google it and did not find an answer.
Could you share your Derivation ?
Thanks
Otto
Re: Consecutive Heads/Tails in weighted coin toss
The chance of getting 20 tails in a row in 20 tosses is 0.320. When tossing a coin 1000 times there is are (1000-20+1)=981 times where you can have 20 of an outcome in a row. Since it is 20 or more we don't care what the other 980 tosses are.
Since the chance of getting 20 in a row in one attempt is 0.320 and you get 981 attempts the chance of getting 20 in a row through the whole 1000 tosses is 981(0.320)
Re: Consecutive Heads/Tails in weighted coin toss
Quote:
Originally Posted by
ottof
Hi, great answer from awkward. But how did you figure out the expected number of runs of R or more successive tails:
![\lambda = p^R \,[(N - R) q + 1]](http://latex.codecogs.com/png.latex?\lambda = p^R \,[(N - R) q + 1])
You derived this formula from scratch. I tried to figure by myself this result and tried to Google it and did not find an answer.
Could you share your Derivation ?
Thanks
Otto
Let

Then
if the first R flips are tails,
but for
,
if the (i-1)st flip is a head and the next R flips are tails.
So
=\Pr(X_1 = 1) = p^R)
and
for 
Applying the theorem that E(X+Y) = E(X) + E(Y), we have
![E \left( \sum_{i=0}^{N-R+1} X_i \right) = \sum_{i=0}^{N-R+1} E(X_i) = p^R + (N-R)q p^R = p^R [(N-R)q + 1]](http://latex.codecogs.com/png.latex?E \left( \sum_{i=0}^{N-R+1} X_i \right) = \sum_{i=0}^{N-R+1} E(X_i) = p^R + (N-R)q p^R = p^R [(N-R)q + 1])