The point estimate $\bar{x}$ for the population mean $m$ is calculated as the sample mean from the actual selection data in Table 1. To find $\bar{x}$, you need to sum up all the values in Table 1 and divide by the total number of observations in that table.

Here’s the general formula for calculating $\bar{x}$:

$\bar{x} = \frac{1}{n} \sum_{i=1}^{n} x_i$

Where:

• $n$ is the total number of data points (sample size).
• $x_i$ are the individual values in the sample (the data from Table 1).

You would:

1. Add up all the values from Table 1.
2. Divide the sum by the total number of values (the sample size) to obtain $\bar{x}$, the sample mean or point estimate of $m$.

If you can provide the values from Table 1, I can help you calculate $\bar{x}$.

Do you have the data from Table 1, or would you like to clarify further?

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Would you like details on how to compute expected value and sample mean from tables? Here are 5 questions that expand on this concept:

1. How does the sample mean $\bar{x}$ relate to the population mean $m$?
2. What is the difference between a point estimate and an interval estimate?
3. Why is the expected value important in probability distributions?
4. Can we assume that the sample mean $\bar{x}$ will always equal the population mean $m$?
5. What factors might cause discrepancies between the sample mean and the expected value?

Tip: When working with large data sets, it's useful to organize the data in a table or spreadsheet to compute sums and averages quickly.