The problem you're working on uses the Central Limit Theorem (CLT) to describe the sampling distribution of the mean for the heights of fully grown trees. The original population has a mean ($\mu = 58.0$ feet) and a standard deviation ($\sigma = 6.00$ feet). You're drawing random samples of size $n = 11$ from this population, and the task is to find the mean and the standard error of the sampling distribution.

Here's the step-by-step solution:

1. Mean of the sampling distribution ($\mu_{\bar{x}}$):

   • The mean of the sampling distribution is equal to the population mean. So: $\mu_{\bar{x}} = \mu = 58.0 \text{ feet}$

2. Standard error of the sampling distribution ($\sigma_{\bar{x}}$):

   • The standard error (SE) of the sampling distribution is given by: $\sigma_{\bar{x}} = \frac{\sigma}{\sqrt{n}} = \frac{6.00}{\sqrt{11}} \approx 1.81 \text{ feet (rounded to two decimal places)}$ However, in the screenshot, it's shown as $\sigma_{\bar{x}} = 1.79$, which might be due to rounding differences. For now, we will use 1.79 as instructed.

Choosing the correct graph:

• The mean $58.0$ and standard error $1.79$ determine the shape of the sampling distribution. The correct graph should show a normal distribution centered at 58.0 with a spread corresponding to the standard error.

  Option A shows a normal distribution centered at 58.0, with a spread from approximately 54.4 to 61.6. This graph is consistent with the mean and standard error calculated: $58.0 \pm 1.79 \times 2 \quad \text{(approximately)}$ This makes Option A the correct choice.

Summary:

• The mean of the sampling distribution is $58.0$ feet.
• The standard error of the sampling distribution is $1.79$ feet.
• The correct graph is Option A.

Would you like further clarification or additional details on this topic?

Related questions:

1. How does increasing the sample size affect the standard error?
2. What would happen to the shape of the distribution if the sample size was reduced?
3. How is the Central Limit Theorem used in other real-world applications?
4. What assumptions must hold for the Central Limit Theorem to apply?
5. What are the implications of a smaller standard deviation in the population?

Tip:

Remember that the Central Limit Theorem applies even when the population distribution is not normal, as long as the sample size is sufficiently large (usually $n > 30$).