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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
23 de oct. de 2020 · Learn what a normal distribution is, how to recognize its characteristics, and how to use the formula to calculate its values. Find out how the central limit theorem and the empirical rule apply to normal distributions and statistics.
En estadística y probabilidad se llama distribución normal, distribución de Gauss, distribución gaussiana, distribución de Laplace-Gauss o normalidad estadística a una de las distribuciones de probabilidad de variable continua que con más frecuencia aparece en estadística y en la teoría de probabilidades. 1 .
Learn about the normal distribution, a continuous probability distribution that plays a central role in probability theory and statistics. Find out its main characteristics, how to compute its parameters and functions, and see density plots and solved exercises.
Learn how to sketch, interpret and apply normal distributions, which have a bell shape and follow the empirical rule. See examples, practice problems and tips on finding percentages and whole counts.
28 de mar. de 2024 · Learn about the normal distribution, the most common distribution function for independent, randomly generated variables. Find out its formula, parameters, applications, history, and relation to the central limit theorem.
The continuous random variable \ (X\) follows a normal distribution if its probability density function is defined as: \ (f (x)=\dfrac {1} {\sigma \sqrt {2\pi}} \text {exp}\left\ {-\dfrac {1} {2} \left (\dfrac {x-\mu} {\sigma}\right)^2\right\}\) for \ (-\infty<x<\infty\), \ (-\infty<\mu<\infty\), and \ (0<\sigma<\infty\).
