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The temporal mean is the arithmetic mean of a series of values over a time period. Assuming equidistant measuring or sampling times, it can be computed as the sum of the values over a period divided by the number of values. A simple moving average can be considered to be a sequence of temporal means over periods of equal duration.
In this example, we would have an average value of 2 for the 1st frame at t=1, 3 for the 2nd frame at t=2, 4 for the 3rd frame at t=3, 6 for the 4th frame at t=4, 4 for the 5th frame at t=5, and 3 for the 6th frame at t=6. Now, these values can be plotted on a graph, where time is on the x-axis, and the averaged concentration values on the y-axis.
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In statistics, the 68–95–99.7 rule, also known as the empirical rule, and sometimes abbreviated 3sr or 3 σ, is a shorthand used to remember the percentage of values that lie within an interval estimate in a normal distribution: approximately 68%, 95%, and 99.7% of the values lie within one, two, and three standard deviations of the mean ...
This is the smallest time after the initial time t 0 that y(t) is equal to one of the critical values forming the boundary of the interval, assuming y 0 is within the interval. Because y(t) proceeds randomly from its initial value to the boundary, τ(y 0) is itself a random variable. The mean of τ(y 0) is the residence time, [1] [2]
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For n = 1 or 2, the midrange and the mean are equal (and coincide with the median), and are most efficient for all distributions. For n = 3, the modified mean is the median, and instead the mean is the most efficient measure of central tendency for values of γ 2 from 2.0 to 6.0 as well as from −0.8 to 2.0.
One is not restricted to using only one of these measures of central tendency. If the data being analyzed is categorical, then the only measure of central tendency that can be used is the mode. However, if the data is numerical in nature (ordinal or interval/ratio) then the mode, median, or mean can all be used to describe the data. Using more ...