QUESTION?
How do you know that the correct answer is 17?
I get the answer N=13.
is then probability of getting at least one of each in N throws.
That sum agrees with which is
Hi everyone.
Here's the question: How many dice do you need to throw so that the probability of one of each face showing (i.e. getting at least one dice to show '1', and at least one to show '2', and... etc.) is at least 0.5?
My answer was 44, which is seriously wrong because the correct answer is 17. I would like to know how to arrive at the correct answer. Anyway, here's how I got my answer.
The possible number of configurations our n dice can have is (n+5) choose 5, i.e. C(n+5, 5) is the notation I will use. I get this by saying: use five dividers to partition the dice into six groups, corresponding to the numbers 1, 2, 3, 4, 5, 6, the partitions telling us what the dice will show on its face.
The number of ways to get at least one of each number showing is to take 6 dice and assume they have the numbers 1,2,...,6 showing. Then configure the remaining n-6 as you please. There are C(n-1, 5) ways to do this.
Therefore for n dice the probability of getting one of each face is C(n-1, 5)/C(n+5, 5). To get this greater than or equal to 0.5 we need n = 44.
As I said, my answer is seriously wrong. I would like to know what is the right way to arrive at the answer, and also, if possible, where I went wrong. Thank you for any help.
What textbook are you using?
This is not an easy problem to grasp.
I will give you a brief version. Say we toss a die ten times.
What is the probability of not getting a 1? It is
That is the same for not getting a 2.
But what is the probability of not getting a 1 or 2?
Is it not .
Thus by subtracting that number from 1, we have the probability of of getting at least one 1 and at least one 2.
That is using inclusion/exclusion.
So the sum I gave in reply #2 is the result of expanding that to all six numbers.
Oh, I see. It is difficult to explain. Basically here's how I understand it.
Call the probability of not getting an i=1,2,...,or 6 in N throws. Then we want . So first we find by finding then and so on.
But everything simplifies because
and similarly for intersections of 3, 4, 5 sets.
Thank you Plato.
Btw, I'm using some old course notes from a friend, I'm trying to learn this stuff on my own. I have never taken an probability/statistics course myself. Is there any book you can recommend?
I just now saw your P.S.
For readability I very much like Jim Pitman's book.
Reviewing probability theory from Casella and Berger "Statistical Inference," I saw an example that reminded me of this (see page 24). I used the same approach and got the same answer Plato did (i.e., 13 throws). The book wanted to know the probability for the event that a gambler could throw at least 1 six in 4 rolls of a die. We get
The last equality follows by independence of rolls. Since the probability of not getting a given numbered face is 5/6, we have
The reason this seemed analogous here was because we're asking the same thing, but for all six of the die, and we want the end result to be an inequality such that P(.) > 0.5. If we instead replace "4" by "k" in the above example, then we want
For k = 12 and k = 13, I got 0.49 and 0.56, respectively. This approach seemed a lot simpler than Plato's above and required no subtraction, which threw me off in his explanation. Thoughts?
On reflection, I wonder if this is correct, though. It could be bad intuition, but I want to say there is a lack of independence between the rolls and we have to discount something (which might raise k to 17, unless we are concluding the given solution was in error). I say this because while this formula works for independent events for a given number to appear in k rolls, each roll has a chance to reveal one of the 6. So I may not, say, roll a six on the first roll, but I an guaranteed to roll something on the first roll. I'm probably wrong, but that kept me from thinking my approach was valid. However, when I got the same answer Plato did, I thought I would comment.