Statistics: geometric distribution proof problem

In summary, the problem asks to show that the probability of a geometrically distributed variable, Y, being an odd integer is equal to p divided by 1-q^2. The solution involves setting up an infinite geometric series and using the formula for the sum of an infinite geometric series to manipulate the equation and arrive at the desired result.
  • #1
TeenieBopper
29
0
Statistics: geometric distribution "proof" problem

Homework Statement


If Y has a geometric distribution with success probability p, show that:

P(Y = an odd integer) = [itex]\frac{p}{1-q^{2}}[/itex]


Homework Equations



p(y)=p(q)[itex]^{2}[/itex]

The Attempt at a Solution



p(1)=pq^0
p(3)=pq^2
p(5)=pq^4
.
.
p(2k+1)=pq^2k

I also know the sum of a geometric series is basically [itex]\frac{first-next}{1-ratio}[/itex]

Basically, I'm stuck on how to something set up so I can do some manipulation to eventually lead to P(Y = an odd integer) = [itex]\frac{p}{1-q^{2}}[/itex]
 
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  • #2


Would you explain your setup in more detail? Where are you drawing numbers from, the Reals, the Integers? How is the selection of numbers done?What is y? What is q? Sorry, I really don't understand what you're doing?
 
  • #3


TeenieBopper said:

Homework Statement


If Y has a geometric distribution with success probability p, show that:

P(Y = an odd integer) = [itex]\frac{p}{1-q^{2}}[/itex]

Homework Equations



p(y)=p(q)[itex]^{2}[/itex]

The Attempt at a Solution



p(1)=pq^0
p(3)=pq^2
p(5)=pq^4
.
.
p(2k+1)=pq^2k

I also know the sum of a geometric series is basically [itex]\frac{first-next}{1-ratio}[/itex]

Basically, I'm stuck on how to something set up so I can do some manipulation to eventually lead to P(Y = an odd integer) = [itex]\frac{p}{1-q^{2}}[/itex]

You want the sum

p(1)+p(3)+p(5)+... = p + pq2 + pq4+...

This is an infinite geometric series. Do you know the formula for the sum of an infinite geometric series? All you need is the first term and the common ratio to figure it out.
 

1. What is the geometric distribution in statistics?

The geometric distribution is a probability distribution that models the number of trials needed to achieve a success in a series of independent trials with a constant probability of success. It is often used to model situations such as the number of times a coin must be flipped to get a head or the number of attempts needed to make a free throw in basketball.

2. How is the geometric distribution calculated?

The probability of success (p) and the number of trials (n) are needed to calculate the geometric distribution. The formula is P(X = k) = (1-p)k-1 * p, where k is the number of trials needed to achieve success. This formula can also be written as P(X = k) = qk-1 * p, where q = 1-p.

3. What is the main property of the geometric distribution?

The main property of the geometric distribution is that it is memoryless, meaning that the probability of success in future trials is not affected by the outcome of previous trials. This property makes it useful for modeling situations where each trial is independent, such as flipping a coin or rolling a die.

4. How is the geometric distribution related to the binomial distribution?

The geometric distribution is a special case of the binomial distribution, where there are only two possible outcomes (success or failure) and the number of trials is fixed at one. In other words, the geometric distribution can be thought of as a single trial of the binomial distribution.

5. What is a common application of the geometric distribution?

The geometric distribution is commonly used in quality control and reliability analysis, where it can be used to model the probability of a product failing after a certain number of uses or the probability of a machine breaking down after a certain number of operating hours. It is also used in gambling and sports analytics to model the probability of a specific outcome occurring in a series of independent events.

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