The following figure shows the distribution of a population with mean 0.6 00 02 04 06 08 Assume that 100 items are randomly sampled from this population and the mean of these 100 items is recorded. We then repeat this process a large number of times. Select the correct figure that represents the distribution of these sample means. 00 02 04 06 08 1.0 Sample Mean 00 02 04 06 08 10 Sample Mean 00 02 06 08 10

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### Understanding Population Distribution and Sample Means

The following figure shows the distribution of a population with a mean of 0.6.

![Population Distribution](path/to/population-distribution.jpg)

This graph is a histogram representing the population distribution, where the x-axis represents the values ranging from 0.0 to 1.0 and the y-axis represents the frequency of these values in the population. The distribution is approximately symmetric and centered around the mean value of 0.6.

#### Sampling from the Population

Assume that 100 items are randomly sampled from this population, and the mean of these 100 items is recorded. We then repeat this process a large number of times.

#### Selecting the Correct Distribution of Sample Means

Below are three figures depicting potential distributions of these sample means. Select the correct figure that represents the distribution of these sample means.

1. ![Sample Mean Histogram 1](path/to/sample-mean1.jpg)
   - This histogram has a mean at approximately 0.6, with a symmetric and narrow shape indicating low variance.

2. ![Sample Mean Histogram 2](path/to/sample-mean2.jpg)
   - This histogram shows a mean around 0.6 but with higher variability compared to the first, indicating slightly more spread.

3. ![Sample Mean Histogram 3](path/to/sample-mean3.jpg)
   - This histogram displays a flatter distribution with more spread around the mean of 0.6, suggesting a higher variance.

Ensure your choice reflects your understanding of how sample means behave according to the Central Limit Theorem, which states that the distribution of the sample means will tend to be normal (Gaussian), with the same mean as the population, but with reduced variability (standard error).

By selecting the correct distribution, you can apply your knowledge of statistics and probability to real-world scenarios involving sampling and estimation.
Transcribed Image Text:### Understanding Population Distribution and Sample Means The following figure shows the distribution of a population with a mean of 0.6. ![Population Distribution](path/to/population-distribution.jpg) This graph is a histogram representing the population distribution, where the x-axis represents the values ranging from 0.0 to 1.0 and the y-axis represents the frequency of these values in the population. The distribution is approximately symmetric and centered around the mean value of 0.6. #### Sampling from the Population Assume that 100 items are randomly sampled from this population, and the mean of these 100 items is recorded. We then repeat this process a large number of times. #### Selecting the Correct Distribution of Sample Means Below are three figures depicting potential distributions of these sample means. Select the correct figure that represents the distribution of these sample means. 1. ![Sample Mean Histogram 1](path/to/sample-mean1.jpg) - This histogram has a mean at approximately 0.6, with a symmetric and narrow shape indicating low variance. 2. ![Sample Mean Histogram 2](path/to/sample-mean2.jpg) - This histogram shows a mean around 0.6 but with higher variability compared to the first, indicating slightly more spread. 3. ![Sample Mean Histogram 3](path/to/sample-mean3.jpg) - This histogram displays a flatter distribution with more spread around the mean of 0.6, suggesting a higher variance. Ensure your choice reflects your understanding of how sample means behave according to the Central Limit Theorem, which states that the distribution of the sample means will tend to be normal (Gaussian), with the same mean as the population, but with reduced variability (standard error). By selecting the correct distribution, you can apply your knowledge of statistics and probability to real-world scenarios involving sampling and estimation.
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