Probability: Coinflips & Variance

In summary, the conversation discusses finding the variance of two variables, X and Y, representing the number of times a head and tail are flipped in succession and the number of times a tail and head are flipped in succession, respectively. The solution involves breaking down the variables into sums of Bernoulli random variables and using the fact that covariance is equal to variance. The conversation concludes with a discussion about difficulties in finding the variance when i is not equal to j, resulting in a negative variance.
  • #1
Kalinka35
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0

Homework Statement


You are flipping a fair coin n times. Let X be the number of times you get a head followed by a tail (HT) and Y is the number of times you get tail and then a head (TH).

Find Var(X) and Var(Y).


Homework Equations


Cov(X,X) = Var(X)


The Attempt at a Solution


My approach is the break up the variables X and Y into sums of Bernoulli random variables. For instance, X = X1 + X2 +...+ Xn-1
(because there are n-1 slots for a HT in n flips).
So Xi = 1 if the ith slot of two flips contains a HT and 0 otherwise
I am using the fact that Cov(X, X) = Var(X) and that
Cov(Xi, Xj) = E(Xi Xj) - E(Xi)E(Xj) and then summing this up over all i and j.

For the case where i=j I got 3/16, which seems reasonable. I am have trouble with the case where i≠j, my answers keeps coming out negative and therefore making my whole variance negative, which is not right.

What I've done is:
Cov(Xi, Xj) = E(Xi Xj) - E(Xi)E(Xj)
= P(Xi Xj =1) - (1/4)(1/4)
= P(Xi = 1, Xj = 1) - (1/16)
Here is seems to me that P(Xi = 1, Xj = 1) = 0 since if you have HT in the ith slot then the jth slot begins with a T and thus cannot be HT so Xj = 0. But then this gives you that Cov(Xi, Xj) for i≠j = -1/16. Then summing up you get

(n-1)(3/16) + (n2 - n)(-1/16)
=(1/16)(-n2 + 4n - 3) and this is not positive for most legitimate values of n.

Can anyone help explain what I am missing? Thanks.
 
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  • #2
Never mind. I think I figured it out.
 

What is probability?

Probability is a measure of the likelihood that a particular event will occur. It is expressed as a number between 0 and 1, with 0 representing impossibility and 1 representing certainty.

How do you calculate the probability of a coin flip?

The probability of a coin flip is calculated by dividing the number of desired outcomes (heads or tails) by the total number of possible outcomes (2, since there are only two sides to a coin). This results in a probability of 0.5 or 50% for each outcome.

What is the difference between theoretical probability and experimental probability?

Theoretical probability is based on mathematical calculations and assumes that all outcomes are equally likely. Experimental probability is based on actual data collected from experiments or real-life events.

What is variance?

Variance is a measure of how spread out a set of data is. In the context of coin flips, it measures how much the actual results differ from the expected results (50% heads and 50% tails).

How can you use coin flips to demonstrate the concept of variance?

By flipping a coin multiple times and recording the results, you can calculate the variance and see how much the actual results deviate from the expected results. The more times you flip the coin, the closer the variance will be to the expected value of 0.

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