Stats - find the distribution function of an infinite sample space.

In summary, for the experiment of rolling a die until a six turns up for the first time, the appropriate infinite sample space is described as Ω = {6, N6, NN6, NNN6, ...}, and the distribution function is given by (5/6)^(n-1) - (1/6). This distribution function satisfies the condition that the sum of all values approaches 1 as n approaches infinity, as desired.
  • #1
dtsar
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Homework Statement



A die is rolled until the first time that a six turns up. We shall see that the
probability that this occurs on the nth roll is (5/6)n−1 · (1/6). Using this fact,
describe the appropriate infinite sample space and distribution function for
the experiment of rolling a die until a six turns up for the first time. Verify
that for your distribution function the Ʃ(ω)=1, as ω→∞

Homework Equations



Relevant equations are in the question.

The Attempt at a Solution


Ω = { 6, N6, NN6, NNN6, ... , N...N6 }
Also, I know that the equation is exponential decay, but I just don't know how to get the formula...
I also know that it adds up, because (1/6)+(5/6)(1/6) + (5/6)^2(1/6) .. etc eventually equal to 1 as N→∞
 
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  • #2
dtsar said:

Homework Statement



A die is rolled until the first time that a six turns up. We shall see that the
probability that this occurs on the nth roll is (5/6)n−1 · (1/6). Using this fact,
describe the appropriate infinite sample space and distribution function for
the experiment of rolling a die until a six turns up for the first time. Verify
that for your distribution function the Ʃ(ω)=1, as ω→∞

Homework Equations



Relevant equations are in the question.


The Attempt at a Solution


Ω = { 6, N6, NN6, NNN6, ... , N...N6 }
Also, I know that the equation is exponential decay, but I just don't know how to get the formula...
I also know that it adds up, because (1/6)+(5/6)(1/6) + (5/6)^2(1/6) .. etc eventually equal to 1 as N→∞

(i) What you wrote was (5/6)n-1 - 1.(1/6), which means (5n/6) - (1/6). You should have written either (5/6)^(n-1) - (1/6) or used the "S U P" button to get (5/6)n-1 - (1/6).
(ii) Saying Ʃ(ω)=1, as ω→∞ makes no sense: the ω need not be numbers, so they can't "go to infinity". Just saying Ʃ(ω)=1 is enough.
(iii) You write Ω as though it has an "end", but it doesn't just write Ω = {6, N6, NN6, NNN6, ... }. Also, if you use N here you should not later say "as N → ∞". Use a different symbol.

Aside from these writing issues, I don't see your problem; you seem to have answered the questions you were asked. For example, when you say "I just don't know how to get the formula...", that is not relevant: you are *given* the formula, and are asked to use it. You have done that correctly.

RGV
 

1. What is a distribution function?

A distribution function, also known as a cumulative distribution function (CDF), is a mathematical function that shows the probability that a random variable takes on a certain value or falls within a certain range.

2. How do you find the distribution function of an infinite sample space?

The distribution function of an infinite sample space can be found by taking the integral of the probability density function (PDF) over the desired range. This integral represents the area under the curve of the PDF, which is equal to the probability of the random variable falling within that range.

3. What is the difference between a discrete and continuous distribution function?

A discrete distribution function is used for random variables that can only take on a finite or countably infinite number of values. A continuous distribution function is used for random variables that can take on any value within a certain range.

4. How do you interpret the values of a distribution function?

The values of a distribution function represent the cumulative probabilities of a random variable taking on values less than or equal to a certain value. This means that the value of the distribution function at a given point is equal to the probability of the random variable falling within that range.

5. What is the relationship between a distribution function and a probability distribution?

A distribution function is the mathematical representation of a probability distribution. It shows the probabilities of all possible outcomes of a random variable, while a probability distribution is a table, graph, or formula that describes the probabilities of each outcome. The distribution function is derived from the probability distribution and can be used to calculate probabilities for specific events.

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