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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.
This standard is for the exchange of essential social and economic statistics, for example between European national agencies and Eurostat and the European Central Bank. SDMX is used for the dissemination of multi-dimensional aggregated data. The Data Documentation Initiative (DDI) was created by the DDI Alliance. DDI is an open metadata ...
It is ubiquitous in nature and statistics due to the central limit theorem: every variable that can be modelled as a sum of many small independent, identically distributed variables with finite mean and variance is approximately normal. The normal-exponential-gamma distribution; The normal-inverse Gaussian distribution
An example of this transmission of information is in fields of advertising, public announcements and speeches. Another way to look at dissemination is that of which it derives from the Latin roots, the scattering of seeds. These seeds are metaphors for voice or words: to spread voice, words, and opinion to an audience.
In probability theory and statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable.The general form of its probability density function is [2] [3] = ().
Considerations of the shape of a distribution arise in statistical data analysis, where simple quantitative descriptive statistics and plotting techniques such as histograms can lead on to the selection of a particular family of distributions for modelling purposes. The normal distribution, often called the "bell curve" Exponential distribution
Producers of official statistics must maintain a reputation of professionalism and independence. The statistical system must be free from interference that could influence decisions on the choice of sources, methods used for data collection, the selection of results to be released as official, and the timing and form of dissemination.
In mathematics and statistics, deviation serves as a measure to quantify the disparity between an observed value of a variable and another designated value, frequently the mean of that variable. Deviations with respect to the sample mean and the population mean (or "true value") are called errors and residuals, respectively.