Understanding Statistical Averages: Methods and Formulas

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Statistical averages, or measures of central tendency, include the mean, median, mode, and mid-range, each serving different purposes in data analysis. The mean is calculated by summing values and dividing by the count, while the median is the middle value in an ordered list, providing a better measure for skewed distributions. The mode identifies the most frequently occurring value, useful for categorical data, and the mid-range is a quick estimate found by averaging the smallest and largest values. The discussion also touches on expected value, which considers all possible outcomes, including infinite distributions, highlighting its significance in statistical modeling. Understanding these concepts is crucial for accurately interpreting data and deriving system models.
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Hello,

In some applications, statistical averages are encountered in the derivation of system models. Practically, how can we find the statistical average? Is there mathematical expression to express the expected value?

Thanks in advance
 
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There are several different kinds of "average", or "measure of central tendency" used in statistics.

Given a finite set of numeric data we can use the

mean: Add the numbers divide by how many numbers there are (i.e. the "arthmetic average" you learned in elementary school).

median: Write the numbers in increasing order and pick the "middle number". Relatively easy to find and if the distribution is not symmetric may "measure the central tendency" better than the mean. (Often used for income distributions where there is a lowest possible income, 0, but no highest possible income.)

mode: The value that occurs most often. Not very "precise" and there often is more than one value that occur the same number of times but has the advantage that no arithmetic or ordering is required so it can be applied to non-numeric "values". For example, if you were interested in an "average" 'favorite color' you would have to use the mode.

mid-range. Add the smallest and largest values and divide by 2. Fast and dirty!
 
Yes, but what about the expected value, where the average takes place over all possible values, which may extent to infinite? For example, for an exponential random variable.
 
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