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Variance has a central role in statistics, ... The resulting estimator is biased, however, and is known as the biased sample variation. Population variance
In statistics, dispersion (also called variability, scatter, or spread) is the extent to which a distribution is stretched or squeezed. [1] Common examples of measures of statistical dispersion are the variance, standard deviation, and interquartile range. For instance, when the variance of data in a set is large, the data is widely scattered.
Climate variability, changes in the components of Earth's climate system and their interactions; Spatial variability, when a quantity that is measured at different spatial locations exhibits values that differ across the locations; Statistical variability, a measure of dispersion in statistics
Statistics is a mathematical body of science that pertains to the collection, analysis, interpretation or explanation, and presentation of data, [5] or as a branch of mathematics. [6] Some consider statistics to be a distinct mathematical science rather than a branch of mathematics. While many scientific investigations make use of data ...
In statistics, the variance function is a smooth function that depicts the variance of a random quantity as a function of its mean.The variance function is a measure of heteroscedasticity and plays a large role in many settings of statistical modelling.
In probability theory and statistics, the coefficient of variation (CV), also known as normalized root-mean-square deviation (NRMSD), percent RMS, and relative standard deviation (RSD), is a standardized measure of dispersion of a probability distribution or frequency distribution.
In statistics, explained variation measures the proportion to which a mathematical model accounts for the variation of a given data set. Often, variation is quantified as variance ; then, the more specific term explained variance can be used.
The term 'random variable' in its mathematical definition refers to neither randomness nor variability [2] but instead is a mathematical function in which the domain is the set of possible outcomes in a sample space (e.g. the set { H , T } {\displaystyle \{H,T\}} which are the possible upper sides of a flipped coin heads H {\displaystyle H} or ...