Geometric Distribution problem

In summary, the largest value of y0 for P(Y > y0) ≥ .1 can be found by using the reverse approach and finding the maximum y0 such that P(Y < y0) < .9. This can be done by using the geometric series and solving for y0. The final equation should be something like 1-.3^(y0) < .9.
  • #1
FaradayLaws
8
0
Question:
If Y has a geometric distribution with success probability .3, what is the largest value, y0, such
that P(Y > y0) ≥ .1?

Attempt:
So i represented the probability of the random variable as a summation

Sum from y0= y0+1 to infinity q^(yo+1)-1 p ≥ .1
using a change of variables i let l = y0+1

p Sum from y0=l to inf (q)^l-1 ≥ .1

from here I'm stuck.. i was thinking of applying the partial sum for the geometric series but I'm not sure how to proceed from here.


Thanks!
 
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  • #2
It is easier to work from the other end. Find max y0 such that P(Y < y0) < .9. You will then have a finite sum (geometric series) to work with.
 
  • #3
oh okay;

once working with the other end =>

summation from y0 =0 to y0-1 of q^y0-1 p < 0.9

with the change of variables l= y0-1

summation from l=0 to l of q^l p < 0.9

now finding the partial sum of the geometric series

p/(1-q) < 0.9
0.3/ 0.3 < 0.9

i'm stuck here ? how do i get the value for y0 ?
 
  • #4
Your partial sum doesn't look right. It should be a function of y0. It should be something like 1-.3^(y0) < .9. (I am not sure whether it should be y0 or y0+1).
 

What is the Geometric Distribution problem?

The Geometric Distribution problem is a mathematical concept that deals with the probability of a specific number of trials needed to achieve a certain outcome in a series of independent events with only two possible outcomes (success or failure).

What are the key components of the Geometric Distribution problem?

The key components of the Geometric Distribution problem are the probability of success in each trial (p), the number of trials needed to achieve the desired outcome (x), and the probability of failure in each trial (1-p).

How is the Geometric Distribution problem solved?

The Geometric Distribution problem is typically solved using the formula P(x) = (1-p)^(x-1) * p, where P(x) is the probability of achieving the desired outcome in x number of trials.

What is the relationship between the Geometric Distribution and the Binomial Distribution?

The Geometric Distribution is a special case of the Binomial Distribution, where the number of trials (n) is fixed at 1. In other words, the Binomial Distribution is used when there are multiple trials, while the Geometric Distribution is used when there is only one trial.

What are some real-life applications of the Geometric Distribution problem?

The Geometric Distribution problem can be applied to various real-life scenarios, such as determining the number of attempts needed to win a game of chance, predicting the number of attempts needed to achieve a desired outcome in a scientific experiment, or estimating the number of attempts needed to successfully complete a certain task. It is also commonly used in quality control and reliability studies.

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