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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.
Learn how to calculate the weighted mean of a set of data points, which is similar to the arithmetic mean but with different weights for each data point. See examples, formulas, statistical properties and applications of the weighted mean in descriptive statistics and other fields.
Learn how to compute and interpret the sample mean and covariance matrix, statistics based on a sample of data on one or more random variables. The sample covariance matrix is an estimate of the population covariance matrix and is useful in judging the reliability of the sample means.
A Malthusian growth model is a mathematical formula that describes exponential population growth based on the idea of the function being proportional to the speed to which the function grows. The model is named after Thomas Robert Malthus, who wrote an influential essay on population dynamics in 1798.
The arithmetic mean is the sum of a collection of numbers divided by the count of numbers in the collection. It is a measure of central tendency that has several properties, such as minimizing the sum of squared deviations and being independent of scale.
Learn about the properties, definitions, and applications of the continuous uniform distribution, a symmetric probability distribution with constant density on a fixed interval. Find the formula for the variance of the distribution and see examples and graphs.
Learn how to choose the number of observations or replicates in a statistical sample based on various factors, such as confidence level, margin of error, and variability. Find formulas and examples for estimating proportions, means, and variances.
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.