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The point () is called the mean value of () on [,]. So we write f ¯ = f ( c ) {\displaystyle {\bar {f}}=f(c)} and rearrange the preceding equation to get the above definition. In several variables, the mean over a relatively compact domain U in a Euclidean space is defined by
The mean of a set of observations is the arithmetic average of the values; however, for skewed distributions, the mean is not necessarily the same as the middle value (median), or the most likely value (mode). For example, mean income is typically skewed upwards by a small number of people with very large incomes, so that the majority have an ...
Truncated mean or trimmed mean the arithmetic mean of data values after a certain number or proportion of the highest and lowest data values have been discarded. Interquartile mean a truncated mean based on data within the interquartile range. Midrange the arithmetic mean of the maximum and minimum values of a data set. Midhinge
The arithmetic mean of a sample is always between the largest and smallest values in that sample. The arithmetic mean of any amount of equal-sized number groups together is the arithmetic mean of the arithmetic means of each group.
The geometric mean of a data set is less than the data set's arithmetic mean unless all members of the data set are equal, in which case the geometric and arithmetic means are equal. This allows the definition of the arithmetic-geometric mean, an intersection of the two which always lies in between.
Average of chords. In ordinary language, an average is a single number or value that best represents a set of data. The type of average taken as most typically representative of a list of numbers is the arithmetic mean – the sum of the numbers divided by how many numbers are in the list.
The arithmetic mean of a population, or population mean, is often denoted μ. [2] The sample mean ¯ (the arithmetic mean of a sample of values drawn from the population) makes a good estimator of the population mean, as its expected value is equal to the population mean (that is, it is an unbiased estimator).
The sample maximum and minimum are the least robust statistics: they are maximally sensitive to outliers.. This can either be an advantage or a drawback: if extreme values are real (not measurement errors), and of real consequence, as in applications of extreme value theory such as building dikes or financial loss, then outliers (as reflected in sample extrema) are important.