How Does the Binomial Coefficient Apply to Multiple Outcome Scenarios?

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In summary, the binomial coefficient can be used to find solutions to problems with many possible outcomes.
  • #1
sherrellbc
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Homework Statement


The binomial coefficient formula can be used in many applications. What I have been learning about is its application in Binomial Distributions.

Define a random variable X to be the number of heads associated with each flip.

Imagine you have, for example, 5 coins. What is the probability that X = 2?
That is, P(X=2)

So, below I have prove how you can use the nCk formula for situations like this one in which you have 1 of two possible outcomes. But what if you were to have many more? Imagine instead of H/T, you have A,B,C,D. How can we apply the nCk in situations like this?

Homework Equations



## {(n k)} = {n!}/{k!(n-k)!} ##


The Attempt at a Solution


Without applying the formula, it is easy to derive(essentially the formula in the process) the solution to the problem.

Given 5 "spaces", the first H has 5 potential slots, then for the next H there remains 4. That is,

_ _ _ _ _
5 4

Which is 5*4 and can be written as, 5!/(5-2)!
Then we want to divide out by all possible permutations of 2 "things", in this case heads.

So, we have:
## {(5 2)} = {5!}/{2!(5-2)!} = 10*{1/2^6}##

-----------------------------------------------------------------------------------------------------------

But, what if we had more possible outcomes? (i.e. A, B, C ... )
The use of nCk in the previous manner worked because we "knew" what the other spaces would hold. That is,

_ _ _ _ _
5 4

We know that the empty spaces here WOULD be tails, since we defined Heads as success.

For example:
H H T T T
H T H H H
H T T H H
H T T T H
...

What if we used the same logic in the case where we have many more possible outcomes?(1, B, C ... )

Redefine X to be: Number of A's
Find P(X=2)
Using the same logic, we resolve the same solution! That:
## {(5 2)} = {5!}/{2!(5-2)!} = 10##
That only difference being that we have no information on probability of A, B, C happening as we did with the Heads/Tails flips.

So, we are essentially saying here that we have 10 UNIQUE combinations of two A's with any other selection of B, C, D .. ect.

How is this possible?


tl;dr, same result with more possible outcomes does not make immediate sense.
 
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  • #2
I do not wish to edit the above post because each time I do all of the formatting quits working.

So, to clarify:
We resolve a solution of 10 unique combinations of in 5 spaces consisting of to A's.

My question is essentially how do we deal how many possible outcomes exist? By this I mean, H/T or A/B/C/D .. etc.

We could have only 6 spaces, but an infinite number of outcomes in the form of A,B,C .. and still resolve 10 in this problem!

What am I not getting here? The binomial application cannot be restricted in this sense to cases in which there are only two possible outcomes? I thought that idea was encapsulated in the Bernoulli Distribution.
 
  • #3
  • #4
awkward said:
The extension of the binomial distribution to more than two outcomes is called the multinomial distribution. See, for example, http://en.wikipedia.org/wiki/Multinomial_distribution.

Perfect, so the Binomial Distribution is for only two outcomes. If so, that makes perfect sense because this was not.

If the Binomial Distribution, in general, is for only two possible outcomes, what is the purpose of the Bernoulli Distribution which is a special case of the Binomial?

I will have to read more on the topic it seems. Thank you.
 
  • #5
sherrellbc said:

Homework Statement


The binomial coefficient formula can be used in many applications. What I have been learning about is its application in Binomial Distributions.

Define a random variable X to be the number of heads associated with each flip.

Imagine you have, for example, 5 coins. What is the probability that X = 2?
That is, P(X=2)

So, below I have prove how you can use the nCk formula for situations like this one in which you have 1 of two possible outcomes. But what if you were to have many more? Imagine instead of H/T, you have A,B,C,D. How can we apply the nCk in situations like this?

You want the multinomial distribution. Look here for one explanation, or use Google for others:

http://onlinestatbook.com/2/probability/multinomial.html

[Edit] Woops, I didn't see the other posts...
 

What is "N choose K" (nCk) usage?

"N choose K" (nCk) usage is a mathematical concept used to calculate the number of possible combinations when selecting a subset of k objects from a larger set of n objects. It is commonly used in probability and statistics, as well as in other fields such as computer science and genetics.

How is "N choose K" (nCk) calculated?

The formula for "N choose K" (nCk) is n! / (k! * (n-k)!), where n! represents the factorial of n (n * (n-1) * (n-2) * ... * 1). This formula can also be expressed as nCk or C(n,k). In simpler terms, "N choose K" calculates the number of ways k objects can be selected from a total of n objects, without regard to the order in which they are selected.

What is the difference between "N choose K" (nCk) and "N choose R" (nCr)?

The only difference between "N choose K" (nCk) and "N choose R" (nCr) is the notation used. Both represent the same concept of selecting a subset of k objects from a larger set of n objects.

What are some real-world applications of "N choose K" (nCk) usage?

"N choose K" (nCk) usage can be applied in various fields, such as genetics to calculate the number of possible offspring genotypes, in sports to determine the number of possible game outcomes, and in computer science to analyze combinations of different variables or inputs.

Are there any special cases or exceptions when using "N choose K" (nCk)?

One special case is when k = 0, in which case the result of "N choose K" (nCk) is always 1. This is because there is only one way to select 0 objects from a set of n objects (by selecting none at all). Additionally, "N choose K" (nCk) is only defined for non-negative integers n and k, and it is not applicable for non-integer values.

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