What is the Distribution of Tossing a Die Until a 6 Appears?

In summary, the conversation was about a discrete probability problem that involved finding the probability distribution of X, the number of tosses needed to obtain the first six when tossing a die until a "6" appears. The solution to part a was a geometric distribution. The conversation also discussed proving that the summation of P(x) from x = 1 to n is equal to 1 and determining the distribution function of X in part c. The TA initially insisted that the summation was up to n, not infinity, and that the distribution function in part d was not the conditional distribution. However, after correcting the question, it was determined that the summation should be taken to infinity and the distribution function can be found by normalizing P(X
  • #1
island-boy
99
0
I got really really confused by this supposedly easy discrete probability problem:

The problem asks:
a)toss a die until a "6" appears. Find the probability distribution of X where X is the number of tosses neded to obtain the first six.
b) Prove that the summation of P(x) from x = 1 to n (not infinity!) is equal to 1.
c) Determine P(X = 2k + 1) for every k an element of a natural number.
d) Find the distribution function of X in c)

Here's what I got:
for a) obviously, the answer is a geometric distribution as the answer is:
P(X=x) = f(x) = (5/6)^(x-1) * (1/6) for x = 0,1,2,3,... and 0 otherwise

for c) I just substitute x with 2k + 1, thus,
P(X = 2k + 1) = (5/6)^(2k) * (1/6) for any k, natural number

what I can't understand is b and d.
How the heck are you suppose to prove b?
since P(x) is a geometric series, then its summation from 1 to n is given by a(1 -r^n)/(1-r) which is equal to 1 - (5/6)^n when a = 1/6 and r = 5/6...this summation is equal only to 1 when n reaches infinity! I asked the TA who gave this question about this, and he keeps insisting it should be up to n, not up to infinity. (if it is up to infinity, the summation is just a/1-r = 1!). Am I just crazy and the TA is right, or can it be proven that the summation up to n is equal to 1?

And for d), is it possible to get the distibution function for X = 2k+1? not the probability (which is c), but the distribution itself. I asked if this is the conditional distribution f(x| x = 2k +1), and the TA says it isn't. It's the distribution itself. Is this even possible? Again, I hope I'm not crazy.

This has been driving me bananas for the last 2 days...

any help is appreciated. thanks
 
Last edited:
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  • #2
First, f(x) is not defined when x = 0. (Or you could define it to be 0)

Your TA appears to be wrong. n is not even defined in the problem, and it does sum to 1 only as x goes to infinity. The distribution function g would be the function satisfying
g(k) = P(X = 2k + 1|X is odd)
To actually find g, you can just normalize your function P(X = 2k + 1) so that the infinite sum is 1.
 
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  • #3
you guys are right. The TA corrected the question in b and changed n to infinity. I am awfully frustrated by the fact though that he only gave this correction 1 day before the homework is due AND even more so that he kept insisting that it is n, not infinity when I asked him about the problem 3 days before.
 

Related to What is the Distribution of Tossing a Die Until a 6 Appears?

1. What is a discrete probability function?

A discrete probability function is a mathematical function that assigns probabilities to every possible outcome of a discrete random variable. It is used to model situations where the outcomes are countable and finite, such as rolling a die or flipping a coin.

2. How is a discrete probability function different from a continuous probability function?

A discrete probability function is used for discrete random variables, where the possible outcomes are countable and finite. A continuous probability function, on the other hand, is used for continuous random variables, where the possible outcomes are infinite and uncountable, such as the height of a person or the temperature outside.

3. What is the difference between a probability mass function and a cumulative distribution function?

A probability mass function (PMF) gives the probability of each individual outcome of a discrete random variable, while a cumulative distribution function (CDF) gives the probability of the random variable being less than or equal to a specific value. In other words, the PMF gives the probabilities for each possible outcome, while the CDF gives the overall probability distribution of the random variable.

4. How do you calculate the mean and variance of a discrete probability function?

The mean of a discrete probability function is calculated by multiplying each possible outcome by its corresponding probability and then summing these values. The variance is calculated by subtracting the mean from each outcome, squaring these differences, multiplying by their probabilities, and then summing these values.

5. How can discrete probability functions be used in real-world applications?

Discrete probability functions are used in a wide range of fields, such as finance, engineering, and biology. They can be used to model and predict outcomes in various situations, such as predicting stock prices or analyzing the effects of a new medication on a population. They are also used in computer science for data analysis and machine learning algorithms.

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