Calculating COV(A,B) for a Coin Throw Experiment

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We first compute E[A|B] since we have a fixed B. A|B is a random variable that has a binomial distribution with p = 1/3 and N = B. So E[A|B] = B*(1/3).Then E[AB|B] = E[A|B]B = B^2/3 so E[AB] = E[E[AB|B]] = E[B^2/3] = (N^2)/3.So Cov(A,B) = (N^2)/3 - (N^2)*2/9 = (N^2)/9.In summary, we have a binomial distribution with p = 1/3 and N
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proaction
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I have a problem with this question
can anyone help me please ..

We throw a coin with two sides, one has 1 on it and the other has 0 on it, the probability of getting 1 is 2/3 and 0 is 1/3 .

now we throw the coin N times , let A be the number of turns we got 0 on them , and be the number of turns we get 1 on them, what's the COV(A,B) ?

thanks
 
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  • #2
proaction said:
I have a problem with this question
can anyone help me please ..

We throw a coin with two sides, one has 1 on it and the other has 0 on it, the probability of getting 1 is 2/3 and 0 is 1/3 .

now we throw the coin N times , let A be the number of turns we got 0 on them , and be the number of turns we get 1 on them, what's the COV(A,B) ?

thanks

Hey there.

Looking at your problem you have a binomial distribution with p = 1/3 and N being the number of trials.

So basically X ~ Binomial(N,1/3)

Then we are given A = number of turns we get a 0 and B is number of turns we get a 1.

For N trials we have 2^N possibilities.

The distribution is going to range from having 0 matches to having N matches.

For distribution A we will use the parameter p = 2/3 which corresponds to the probability function being NCk (2/3)^(N-k) * (1/3)^k where k is the number of '0's we get.

B is simply a binomial distribution with p = 1/3 which corresponds to the probability function being NCk (1/3)^(N-k) * (2/3)^k.

So A ~ Binomial(N,2/3) and B ~ Binomial(N,1/3)

The definition of covariance is defined to be Cov(A,B) = E[AB] - E[A]E.

We know that E[A] = N*2/3 and E = N*1/3 so E[A]E = (N^2) * (2/9)

As for E[AB] we can use we formula for conditional expectation.
 

1. What is the formula for calculating COV(A,B)?

The formula for calculating the covariance between two variables, A and B, is COV(A,B) = Σ[(A-μA)(B-μB)], where Σ represents the sum of the products of the deviations from the mean for each variable.

2. How do you interpret the value of COV(A,B)?

The value of COV(A,B) indicates the direction and strength of the linear relationship between variables A and B. A positive value indicates a positive relationship, meaning that as one variable increases, the other tends to increase as well. A negative value indicates a negative relationship, meaning that as one variable increases, the other tends to decrease. The magnitude of the value indicates the strength of the relationship, with larger values indicating a stronger relationship.

3. Can COV(A,B) be used to determine causation?

No, covariance cannot be used to determine causation. It only measures the direction and strength of the linear relationship between two variables. Additional research and analysis is needed to establish a causal relationship between variables.

4. How does the sample size affect the calculation of COV(A,B)?

The larger the sample size, the more accurate the calculation of COV(A,B) will be. With a larger sample size, the estimate of the population mean will be more precise, resulting in a more accurate calculation of the deviations from the mean for each variable.

5. Is there a limit to the range of values for COV(A,B)?

No, there is no limit to the range of values for COV(A,B). The value can range from negative infinity to positive infinity, depending on the strength of the relationship between the two variables. However, it is more common to see values between -1 and 1, with a value of 0 indicating no linear relationship between the variables.

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