A population of values has a normal distribution with \( \mu=26.1 \) and \( \sigma=91.3 \). A randorn sample of size \( n=130 \) is drawn. a. What is the mean of the distribution of sample means? \( \mu_{\bar{x}}= \) b. What is the standard deviation of the distribution of sample means? Round your answer to two decimal places. \( \sigma_{\bar{x}}= \)
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The mean of the distribution of sample means, also known as the expected value, is equal to the population mean. So in this case, \( \mu_{\bar{x}} = 26.1 \). This means that if you were to take many random samples from this population and calculate their means, those means would centered around 26.1! To find the standard deviation of the distribution of sample means, we use the formula \( \sigma_{\bar{x}} = \frac{\sigma}{\sqrt{n}} \). Plugging in our values, we get \( \sigma_{\bar{x}} = \frac{91.3}{\sqrt{130}} \approx 8.03 \) (rounded to two decimal places). This tells us how much the sample means will vary from the population mean!