Geometric distribution problem

In summary: In this case, the expected value E(x) is given by: E(X) = \sum\limits_{k = 1}^{\inf} k*p*(1 - p)^{k - 1} E(X) = p*\sum\limits_{k = 1}^{\inf} k*(1 - p)^{k - 1} \ \ \color{blue} Eq:1 E(X) = p*\sum\limits_{k = 0}^{\inf} (k + 1)*(1 - p)^{k} \ \ \ \color{blue} Eq:2 Now subtract Eq #1 from Eq #2: E(X) -
  • #1
semidevil
157
2
a couple decides that they will have kids until a girl is born. the outcome of each birth is independent event, and the probability that a girl will be born is 1/2. The birht at which the first girl appears is a geometric distribution. what is the expected family size.

ok, so we know that the probability of having a girl is 1/2.

geometric distribution formula, it is the sum from 1 to k of (1- p) * (p)^k, where k = 1, 2, 3, 4,...k.

but when I tihnk about it, I have an expected value formula, where E(X) = 1/p. so if I put 1/(1/2), I get the answer is 2. So the expected family size is 2?

I don't know..I have this geometric formula that I don't know what to do with, and I have this expected value formula that makes it seem this problem is too easy...

any tips?
 
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  • #2
the answer is 2...
don't over-complicated the problem
 
  • #3
An important point is there are actually several slightly different forms of the Geometric Distribution, and each has a slightly different E(x). For example, the E(x) you quoted in your msg is NOT correct for the specific Discrete Geometric Density function you presented.

Let's begin with the Geometric Density function you presented, which indicates the probability that success will be achieved on the (k+1)-th trial after "k" failures for an event whose probability of success is (0 < p < 1):

[tex] P(X=k) = p*(1 - p)^{k} \ \ \ k=0,1,2,3,...[/tex]

The expected value E(x) is then given by:

[tex] E(X) = \sum\limits_{k = 0}^{\inf} k*p*(1 - p)^{k} [/tex]
[tex] E(X) = p*\sum\limits_{k = 0}^{\inf} k*(1 - p)^{k} \ \ \color{green} Eq:1 [/tex]
[tex] E(X) = p*\sum\limits_{k = 1}^{\inf} k*(1 - p)^{k} \ \ \ \color{green} Eq:2 [/tex]

Multiply both sides of Eq #1 by (1 - p):

[tex] (1 - p)*E(X) = p*\sum\limits_{k = 0}^{\inf} k*(1 - p)^{k+1} [/tex]
[tex] (1 - p)*E(X) = p*\sum\limits_{k = 1}^{\inf} (k - 1)*(1 - p)^{k} \ \ \ \color{green} Eq:3[/tex]

Now subtract Eq #3 from Eq #2:

[tex] E(X) - (1 - p)*E(X) = p*\sum\limits_{k = 1}^{\inf} [(k) - (k - 1)]*(1 - p)^{k} [/tex]
[tex] p*E(X) = p*\sum\limits_{k = 1}^{\inf} (1 - p)^{k} [/tex]
[tex] E(X) = \sum\limits_{k = 1}^{\inf} (1 - p)^{k} \ \ \ \color{green} Eq:4[/tex]

The infinite sum in Eq #4 is a geometric series with term ratio (1 - p), so that it may be written:

[tex] E(X) = \frac {1 - p} {1 - (1 - p)} [/tex]
[tex] E(X) = \frac {1 - p} {p} [/tex]

Thus, E(X) for your presented Geometric Distribution is (1-p)/p and NOT (1/p). However, the conclusions are the same. The average family will consist of (1-p)/p members PLUS 1 MORE because your presented distribution indicated the probability of "k" failures (with success being at the (k+1)-th trial), thus indicating:

[tex] (Average \ Family \ Size) = E(X) + 1 = 1 + \frac {1 - p} {p} [/tex]

Thus, for p=(1/2):

[tex] \color{red} (Average \ Family \ Size \ for \ p=(1/2)) = 2 [/tex]

Incidentally, your quoted E(X)=(1/p) corresponds to the following form of the Geometric Distribution:

[tex] P(X=k) = p*(1 - p)^{k - 1} \ \ \ \ k = 1,2,3,... [/tex]
 
Last edited:

1. What is a geometric distribution?

A geometric distribution is a type of 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 describe events such as the number of coin flips needed to get a heads or the number of attempts needed to make a free throw in basketball.

2. How is the geometric distribution different from the binomial distribution?

The geometric distribution only considers the number of trials needed to achieve a single success, while the binomial distribution takes into account the number of successes in a fixed number of trials. Additionally, the probability of success remains constant in the geometric distribution, while it may change in the binomial distribution.

3. What is the formula for calculating the probability in a geometric distribution?

The formula for calculating the probability in a geometric distribution is P(X=k) = (1-p)^(k-1) * p, where k is the number of trials needed to achieve a success and p is the probability of success in each trial.

4. How is the mean of a geometric distribution calculated?

The mean of a geometric distribution is calculated by dividing 1 by the probability of success, or 1/p. This represents the average number of trials needed to achieve a success.

5. What real-life scenarios can be modeled using the geometric distribution?

The geometric distribution can be used to model many real-life scenarios, such as the number of attempts needed to make a free throw in basketball, the number of sales calls needed to make a sale, and the number of patients needed to be tested to find one with a rare disease. It can also be used in quality control to determine the number of defective products in a sample.

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