Questions: A family has three children. If the genders of these children are listed in the order they are born, there are eight possible outcomes: BBB, BBG, BGB, BGG, GBB, GBG, GGB, and GGG. Assume these outcomes are equally likely. Let x represent the number of children that are girls. Find the probability distribution of x. Part 1 of 2 (a) Find the number of possible values for the random variable X. There are possible values for the random variable X.

A family has three children. If the genders of these children are listed in the order they are born, there are eight possible outcomes: BBB, BBG, BGB, BGG, GBB, GBG, GGB, and GGG. Assume these outcomes are equally likely. Let x represent the number of children that are girls. Find the probability distribution of x.

Part 1 of 2
(a) Find the number of possible values for the random variable X.

There are  possible values for the random variable X.
Transcript text: A family has three children. If the genders of these children are listed in the order they are born, there are eight possible outcomes: BBB, BBG, BGB, BGG, GBB, GBG, GGB, and GGG. Assume these outcomes are equally likely. Let $x$ represent the number of children that are girls. Find the probability distribution of $x$. Part 1 of 2 (a) Find the number of possible values for the random variable $X$. There are $\square$ possible values for the random variable $X$.
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Solution

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Solution Steps

Step 1: Possible Values for the Random Variable \( X \)

The random variable \( X \) represents the number of girls in a family with three children. The possible outcomes for the genders of the children are:

  • \( BBB \) (0 girls)
  • \( BBG \) (1 girl)
  • \( BGB \) (1 girl)
  • \( BGG \) (2 girls)
  • \( GBB \) (1 girl)
  • \( GBG \) (2 girls)
  • \( GGB \) (2 girls)
  • \( GGG \) (3 girls)

Counting the unique values of \( X \), we find that there are 4 possible values: \( 0, 1, 2, 3 \).

Step 2: Mean of the Distribution

The mean \( \mu \) of the distribution is calculated as follows:

\[ \mu = E(X) = 0 \times 0.125 + 1 \times 0.125 + 1 \times 0.125 + 2 \times 0.125 + 1 \times 0.125 + 2 \times 0.125 + 2 \times 0.125 + 3 \times 0.125 = 1.5 \]

Step 3: Variance of the Distribution

The variance \( \sigma^2 \) is calculated using the formula:

\[ \sigma^2 = E(X^2) - (E(X))^2 \]

Calculating \( E(X^2) \):

\[ E(X^2) = (0 - 1.5)^2 \times 0.125 + (1 - 1.5)^2 \times 0.125 + (1 - 1.5)^2 \times 0.125 + (2 - 1.5)^2 \times 0.125 + (1 - 1.5)^2 \times 0.125 + (2 - 1.5)^2 \times 0.125 + (2 - 1.5)^2 \times 0.125 + (3 - 1.5)^2 \times 0.125 = 0.75 \]

Thus, the variance is:

\[ \sigma^2 = 0.75 \]

Step 4: Standard Deviation of the Distribution

The standard deviation \( \sigma \) is the square root of the variance:

\[ \sigma = \sqrt{\sigma^2} = \sqrt{0.75} \approx 0.866 \]

Final Answer

The number of possible values for the random variable \( X \) is \( 4 \), the mean is \( 1.5 \), the variance is \( 0.75 \), and the standard deviation is \( 0.866 \).

\[ \boxed{4} \]

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