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Explain any two measures of varaibility.
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Asked 12/4/2008 9:56:49 AM
Updated 4/25/2023 12:39:07 AM
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User: Explain any two measures of varaibility.

Weegy: If random variable X has expected value (mean) µ = E(X), then the variance Var(X) of X is given by: This definition encompasses random variables that are discrete, continuous, or neither. [ Of all the points about which squared deviations could have been calculated, the mean produces the minimum value for the averaged sum of squared deviations. The variance of random variable X is typically designated as Var(X), , or simply s2. If a distribution does not have an expected value, as is the case for the Cauchy distribution, it does not have a variance either. Many other distributions for which the expected value does exist do not have a finite variance because the relevant integral diverges. An example is a Pareto distribution whose Pareto index k satisfies 1 < k = 2. [edit] Continuous case If the random variable X is continuous with probability density function p(x), where and where the integrals are definite integrals taken for x ranging over the range of X. [edit] Discrete case If the random variable X is discrete with probability mass function x1 ? p1, ..., xn ? pn, (Note: this variance should be divided by the sum of weights in the case of a discrete weighted variance.) That is, it is the expected value of the square of the deviation of X from its own mean. In plain language, it can be expressed as "The average of the square of the distance of each data point from the mean". It is thus the mean squared deviation. ]

User: What is skewness? discuss its significance in measurements.

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Asked 12/4/2008 9:56:49 AM
Updated 4/25/2023 12:39:07 AM
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Skewness is a measure of symmetry, or more precisely, the lack of symmetry.
Added 4/25/2023 12:39:07 AM
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