Sum of squares — is a concept that permeates much of inferential statistics and descriptive statistics. More properly, it is the sum of the squared deviations . Mathematically, it is an unscaled, or unadjusted measure of dispersion (also called variability). When … Wikipedia
Explained sum of squares — In statistics, an explained sum of squares (ESS) is the sum of squared predicted values in a standard regression model (for example y {i}=a+bx {i}+epsilon {i}), where y {i} is the response variable, x {i} is the explanatory variable, a and b are… … Wikipedia
Residual sum of squares — In statistics, the residual sum of squares (RSS) is the sum of squares of residuals. It is the discrepancy between the data and our estimation model. The smaller this discrepancy is, the better the estimation will be.:RSS = sum {i=1}^n (y i f(x… … Wikipedia
Total least squares — The bivariate (Deming regression) case of Total Least Squares. The red lines show the error in both x and y. This is different from the traditional least squares method which measures error parallel to the y axis. The case shown, with deviations… … Wikipedia
Ordinary least squares — This article is about the statistical properties of unweighted linear regression analysis. For more general regression analysis, see regression analysis. For linear regression on a single variable, see simple linear regression. For the… … Wikipedia
Total harmonic distortion — The total harmonic distortion, or THD, of a signal is a measurement of the harmonic distortion present and is defined as the ratio of the sum of the powers of all harmonic components to the power of the Fundamental frequency.Lesser THD, for… … Wikipedia
Least squares — The method of least squares is a standard approach to the approximate solution of overdetermined systems, i.e., sets of equations in which there are more equations than unknowns. Least squares means that the overall solution minimizes the sum of… … Wikipedia
Linear least squares (mathematics) — This article is about the mathematics that underlie curve fitting using linear least squares. For statistical regression analysis using least squares, see linear regression. For linear regression on a single variable, see simple linear regression … Wikipedia
Linear least squares/Proposed — Linear least squares is an important computational problem, that arises primarily in applications when it is desired to fit a linear mathematical model to observations obtained from experiments. Mathematically, it can be stated as the problem of… … Wikipedia
Non-linear least squares — is the form of least squares analysis which is used to fit a set of m observations with a model that is non linear in n unknown parameters (m > n). It is used in some forms of non linear regression. The basis of the method is to… … Wikipedia
