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A normal distribution is sometimes informally called a bell curve. [8] However, many other distributions are bell-shaped (such as the Cauchy , Student's t , and logistic distributions). (For other names, see Naming .)
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
The Bell Curve: Intelligence and Class Structure in American Life is a 1994 book by the psychologist Richard J. Herrnstein and the political scientist Charles Murray in which the authors argue that human intelligence is substantially influenced by both inherited and environmental factors and that it is a better predictor of many personal outcomes, including financial income, job performance ...
Normal distributions are symmetrical, bell-shaped distributions that are useful in describing real-world data. The standard normal distribution, represented by Z , is the normal distribution having a mean of 0 and a standard deviation of 1.
The graph of a Gaussian is a characteristic symmetric "bell curve" shape. The parameter a is the height of the curve's peak, b is the position of the center of the peak, and c (the standard deviation, sometimes called the Gaussian RMS width) controls the width of the "bell".
The Gaussian function is the archetypal example of a bell shaped function. A bell-shaped function or simply 'bell curve' is a mathematical function having a characteristic "bell"-shaped curve. These functions are typically continuous or smooth, asymptotically approach zero for large negative/positive x, and have a single, unimodal maximum at ...
The two points of the curve that are one standard deviation from the mean are also the inflection points. The central limit theorem states that the distribution of an average of many independent, identically distributed random variables tends toward the famous bell-shaped normal distribution with a probability density function of
Benford's law, which describes the frequency of the first digit of many naturally occurring data. The ideal and robust soliton distributions. Zipf's law or the Zipf distribution. A discrete power-law distribution, the most famous example of which is the description of the frequency of words in the English language.