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A normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable. It is also called a bell curve and has many applications in statistics and sciences. Learn about its parameters, density, cumulative distribution function, and related functions.
A probability density function (PDF) is a function that provides a relative likelihood of a continuous random variable taking a value. Learn the definition, examples, properties and applications of PDFs in probability theory and statistics.
Population density is the number of people per unit of land area, usually per square kilometre or square mile. Learn about the factors, effects and examples of population density, and compare the most densely populated countries and territories in the world.
A probability distribution is a mathematical function that gives the probabilities of occurrence of possible outcomes for an experiment. Learn about different types of probability distributions, such as discrete, continuous, univariate and multivariate, and their properties and applications.
The standard measure of a distribution's kurtosis, originating with Karl Pearson, [1] is a scaled version of the fourth moment of the distribution. This number is related to the tails of the distribution, not its peak; [2] hence, the sometimes-seen characterization of kurtosis as "peakedness" is incorrect.
Learn about the t distribution, a continuous probability distribution that generalizes the normal distribution and is used in statistical tests and confidence intervals. Find out its history, definition, properties, moments, and special cases.
Learn about density estimation, a statistical method to construct an estimate of an unobservable probability density function based on observed data. See how kernel density estimation, a non-parametric technique, is used in various applications and examples.
In statistics, probability theory, and information theory, a statistical distance quantifies the distance between two statistical objects, which can be two random variables, or two probability distributions or samples, or the distance can be between an individual sample point and a population or a wider sample of points.