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36 relations: Average, Bart Kosko, Bessel's correction, Bias of an estimator, Covariance, Covariance matrix, Estimation of covariance matrices, Estimator, Interquartile range, Location parameter, Matrix (mathematics), Maximum likelihood estimation, Mean, Median, Multivariate random variable, Normal distribution, Normalizing constant, Outlier, Positive-definite matrix, Probability distribution, Quantile, Random variable, Realization (probability), Robust statistics, Sample (statistics), Scatter matrix, Statistic, Statistical dispersion, Statistical population, Trimmed estimator, Truncated mean, Unbiased estimation of standard deviation, Vector (mathematics and physics), Weighted arithmetic mean, Winsorized mean, Winsorizing.
Average
In colloquial language, an average is a middle or typical number of a list of numbers.
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Bart Kosko
Bart Andrew Kosko (born February 7, 1960) is a writer and professor of electrical engineering and law at the University of Southern California (USC).
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Bessel's correction
In statistics, Bessel's correction is the use of n − 1 instead of n in the formula for the sample variance and sample standard deviation, where n is the number of observations in a sample.
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Bias of an estimator
In statistics, the bias (or bias function) of an estimator is the difference between this estimator's expected value and the true value of the parameter being estimated.
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Covariance
In probability theory and statistics, covariance is a measure of the joint variability of two random variables.
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Covariance matrix
In probability theory and statistics, a covariance matrix (also known as dispersion matrix or variance–covariance matrix) is a matrix whose element in the i, j position is the covariance between the i-th and j-th elements of a random vector.
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Estimation of covariance matrices
In statistics, sometimes the covariance matrix of a multivariate random variable is not known but has to be estimated.
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Estimator
In statistics, an estimator is a rule for calculating an estimate of a given quantity based on observed data: thus the rule (the estimator), the quantity of interest (the estimand) and its result (the estimate) are distinguished.
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Interquartile range
In descriptive statistics, the interquartile range (IQR), also called the midspread or middle 50%, or technically H-spread, is a measure of statistical dispersion, being equal to the difference between 75th and 25th percentiles, or between upper and lower quartiles, IQR.
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Location parameter
In statistics, a location family is a class of probability distributions that is parametrized by a scalar- or vector-valued parameter x_0, which determines the "location" or shift of the distribution.
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Matrix (mathematics)
In mathematics, a matrix (plural: matrices) is a rectangular array of numbers, symbols, or expressions, arranged in rows and columns.
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Maximum likelihood estimation
In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of a statistical model, given observations.
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Mean
In mathematics, mean has several different definitions depending on the context.
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Median
The median is the value separating the higher half of a data sample, a population, or a probability distribution, from the lower half.
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Multivariate random variable
In probability, and statistics, a multivariate random variable or random vector is a list of mathematical variables each of whose value is unknown, either because the value has not yet occurred or because there is imperfect knowledge of its value.
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Normal distribution
In probability theory, the normal (or Gaussian or Gauss or Laplace–Gauss) distribution is a very common continuous probability distribution.
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Normalizing constant
The concept of a normalizing constant arises in probability theory and a variety of other areas of mathematics.
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Outlier
In statistics, an outlier is an observation point that is distant from other observations.
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Positive-definite matrix
In linear algebra, a symmetric real matrix M is said to be positive definite if the scalar z^Mz is strictly positive for every non-zero column vector z of n real numbers.
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Probability distribution
In probability theory and statistics, a probability distribution is a mathematical function that provides the probabilities of occurrence of different possible outcomes in an experiment.
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Quantile
In statistics and probability quantiles are cut points dividing the range of a probability distribution into contiguous intervals with equal probabilities, or dividing the observations in a sample in the same way.
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Random variable
In probability and statistics, a random variable, random quantity, aleatory variable, or stochastic variable is a variable whose possible values are outcomes of a random phenomenon.
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Realization (probability)
In probability and statistics, a realization, observation, or observed value, of a random variable is the value that is actually observed (what actually happened).
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Robust statistics
Robust statistics are statistics with good performance for data drawn from a wide range of probability distributions, especially for distributions that are not normal.
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Sample (statistics)
In statistics and quantitative research methodology, a data sample is a set of data collected and/or selected from a statistical population by a defined procedure.
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Scatter matrix
In multivariate statistics and probability theory, the scatter matrix is a statistic that is used to make estimates of the covariance matrix, for instance of the multivariate normal distribution.
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Statistic
A statistic (singular) or sample statistic is a single measure of some attribute of a sample (e.g. its arithmetic mean value).
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Statistical dispersion
In statistics, dispersion (also called variability, scatter, or spread) is the extent to which a distribution is stretched or squeezed.
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Statistical population
In statistics, a population is a set of similar items or events which is of interest for some question or experiment.
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Trimmed estimator
In statistics, a trimmed estimator is an estimator derived from another estimator by excluding some of the extreme values, a process called truncation.
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Truncated mean
A truncated mean or trimmed mean is a statistical measure of central tendency, much like the mean and median.
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Unbiased estimation of standard deviation
In statistics and in particular statistical theory, unbiased estimation of a standard deviation is the calculation from a statistical sample of an estimated value of the standard deviation (a measure of statistical dispersion) of a population of values, in such a way that the expected value of the calculation equals the true value.
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Vector (mathematics and physics)
When used without any further description, vector usually refers either to.
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Weighted arithmetic mean
The weighted arithmetic mean is similar to an ordinary arithmetic mean (the most common type of average), except that instead of each of the data points contributing equally to the final average, some data points contribute more than others.
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Winsorized mean
A winsorized mean is a winsorized statistical measure of central tendency, much like the mean and median, and even more similar to the truncated mean.
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Winsorizing
Winsorizing or winsorization is the transformation of statistics by limiting extreme values in the statistical data to reduce the effect of possibly spurious outliers.
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Empirical mean, Sample covariance, Sample covariance matrix, Sample mean, Sample mean and sample covariance, Weighted covariance matrix, Weighted sample.
References
[1] https://en.wikipedia.org/wiki/Sample_mean_and_covariance
