Coin Flipping: Binomial Distribution and Expected Product

In summary, the expected product of the number of heads and number of tails when rolling a fair coin 10 times can be calculated using the formula E(k(10-k)) where k represents the number of heads thrown. Using the binomial distribution formulas, the expected product is equal to 22.5. Both the original answer of 25 and the alternative method are correct.
  • #1
WCMU101
14
0
Question is:

"If you roll a fair coin 10 times what is the expected product of number of heads and number of tails?"

Someone answered 25 at at glassdoor.com. My answer would be:

E(k(10-k)) where k is the rv representing the number of heads thrown.
= 10E(k) - E(k^2)
= 10*mean - (var + mean^2)

where mean = np = 10*0.5 = 5, and var = npq = 10*.5*.5 = 2.5 (these are the formulas for the binomial distribution), thus,
= 10*5 - (2.5 + 25) = 22.5

Who is correct?

Thanks.

Nick.
 
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  • #2
You are right. Another way:
[itex]\sum[/itex]r(n-r).[itex]^{n}[/itex]C[itex]_{r}[/itex] =
[itex]\sum[/itex]n!/(r-1)!/(n-r-1)! =
[itex]\sum[/itex]n(n-1)(n-2)!/(r-1)!/(n-r-1)! =
n(n-1)[itex]\sum[/itex][itex]^{n-2}[/itex]C[itex]_{r-1}[/itex] =
n(n-1).2^(n-2)
Dividing by 2^n to get the average:
n(n-1)/4
For n = 10 this gives 22.5
 

1. How can the binomial distribution be used to model coin flipping?

The binomial distribution can be used to model coin flipping by considering each coin flip as a trial with two possible outcomes (heads or tails) and a fixed probability of success (0.5 for a fair coin). The number of heads in a series of n coin flips can then be calculated using the binomial probability formula.

2. What is the expected product of two coin flips?

The expected product of two coin flips is calculated by multiplying the individual probabilities of each outcome. For a fair coin, the expected product would be (0.5 x 0.5) = 0.25. This means that in the long run, we can expect to get a product of 0.25 for every two coin flips.

3. How does the expected product change with more coin flips?

As the number of coin flips increases, the expected product also increases. This is because the probability of getting the same outcome for each coin flip decreases as the number of trials increases. Therefore, the expected product becomes more spread out, with a higher chance of getting a larger product.

4. Can the binomial distribution be used for biased coins?

Yes, the binomial distribution can be used for biased coins as long as the probability of success (p) is known and remains constant for each trial. The expected product would then be calculated by multiplying the individual probabilities of each outcome, such as (0.3 x 0.7) = 0.21 for a coin with a 30% chance of landing on heads.

5. How is the binomial distribution related to the normal distribution?

The binomial distribution can be approximated by the normal distribution when the number of trials (n) is large enough. This is known as the normal approximation to the binomial distribution. As n increases, the shape of the binomial distribution becomes more bell-shaped and symmetric, resembling the shape of the normal distribution. This approximation can be useful for calculating probabilities for large numbers of coin flips.

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