37 relations: Cambridge University Press, Circumflex, Cook's distance, Covariance, Covariance matrix, David A. Freedman, Dependent and independent variables, Design matrix, Eigenvalues and eigenvectors, Errors and residuals, Fixed effects model, Idempotent matrix, Identity matrix, Independent and identically distributed random variables, Influential observation, Kernel regression, Leverage (statistics), Linear algebra, Linear filter, Linear least squares (mathematics), Linear model, Local regression, Mathematical model, Mean and predicted response, Projection (linear algebra), Propagation of uncertainty, Rank (linear algebra), Robust statistics, Row and column spaces, Smoothing spline, Sparse matrix, Statistics, Studentized residual, Symmetric matrix, The American Statistician, Trace (linear algebra), Variance.
Cambridge University Press (CUP) is the publishing business of the University of Cambridge.
The circumflex is a diacritic in the Latin, Greek and Cyrillic scripts that is used in the written forms of many languages and in various romanization and transcription schemes.
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In statistics, Cook's distance or Cook's D is a commonly used estimate of the influence of a data point when performing a least-squares regression analysis.
In probability theory and statistics, covariance is a measure of the joint variability of two random variables.
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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.
David Amiel Freedman (5 March 1938 – 17 October 2008) was Professor of Statistics at the University of California, Berkeley.
In mathematical modeling, statistical modeling and experimental sciences, the values of dependent variables depend on the values of independent variables.
In statistics, a design matrix, also known as model matrix or regressor matrix, is a matrix of values of explanatory variables of a set of objects, often denoted by X. Each row represents an individual object, with the successive columns corresponding to the variables and their specific values for that object.
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In linear algebra, an eigenvector or characteristic vector of a linear transformation is a non-zero vector that changes by only a scalar factor when that linear transformation is applied to it.
In statistics and optimization, errors and residuals are two closely related and easily confused measures of the deviation of an observed value of an element of a statistical sample from its "theoretical value".
In statistics, a fixed effects model is a statistical model in which the model parameters are fixed or non-random quantities.
In linear algebra, an idempotent matrix is a matrix which, when multiplied by itself, yields itself.
In linear algebra, the identity matrix, or sometimes ambiguously called a unit matrix, of size n is the n × n square matrix with ones on the main diagonal and zeros elsewhere.
In probability theory and statistics, a sequence or other collection of random variables is independent and identically distributed (i.i.d. or iid or IID) if each random variable has the same probability distribution as the others and all are mutually independent.
In statistics, an influential observation is an observation for a statistical calculation whose deletion from the dataset would noticeably change the result of the calculation.
Kernel regression is a non-parametric technique in statistics to estimate the conditional expectation of a random variable.
In statistics and in particular in regression analysis, leverage is a measure of how far away the independent variable values of an observation are from those of the other observations.
Linear algebra is the branch of mathematics concerning linear equations such as linear functions such as and their representations through matrices and vector spaces.
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Linear filters process time-varying input signals to produce output signals, subject to the constraint of linearity.
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In statistics and mathematics, linear least squares is an approach to fitting a mathematical or statistical model to data in cases where the idealized value provided by the model for any data point is expressed linearly in terms of the unknown parameters of the model.
In statistics, the term linear model is used in different ways according to the context.
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LOESS and LOWESS (locally weighted scatterplot smoothing) are two strongly related non-parametric regression methods that combine multiple regression models in a ''k''-nearest-neighbor-based meta-model.
A mathematical model is a description of a system using mathematical concepts and language.
In linear regression, mean response and predicted response are values of the dependent variable calculated from the regression parameters and a given value of the independent variable.
In linear algebra and functional analysis, a projection is a linear transformation P from a vector space to itself such that.
In statistics, propagation of uncertainty (or propagation of error) is the effect of variables' uncertainties (or errors, more specifically random errors) on the uncertainty of a function based on them.
In linear algebra, the rank of a matrix A is the dimension of the vector space generated (or spanned) by its columns.
Robust statistics are statistics with good performance for data drawn from a wide range of probability distributions, especially for distributions that are not normal.
In linear algebra, the column space (also called the range or '''image''') of a matrix A is the span (set of all possible linear combinations) of its column vectors.
Smoothing splines are function estimates, \hat f(x), obtained from a set of noisy observations y_i of the target f(x_i), in order to balance a measure of goodness of fit of \hat f(x_i) to y_i with a derivative based measure of the smoothness of \hat f(x).
In numerical analysis and computer science, a sparse matrix or sparse array is a matrix in which most of the elements are zero.
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Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data.
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In statistics, a studentized residual is the quotient resulting from the division of a residual by an estimate of its standard deviation.
In linear algebra, a symmetric matrix is a square matrix that is equal to its transpose.
The American Statistician is a quarterly peer-reviewed scientific journal covering statistics published by Taylor & Francis on behalf of the American Statistical Association.
In linear algebra, the trace of an n-by-n square matrix A is defined to be the sum of the elements on the main diagonal (the diagonal from the upper left to the lower right) of A, i.e., where aii denotes the entry on the ith row and ith column of A. The trace of a matrix is the sum of the (complex) eigenvalues, and it is invariant with respect to a change of basis.
In probability theory and statistics, variance is the expectation of the squared deviation of a random variable from its mean.
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