Noun:

লিস্ট স্কোয়ার,

least squares's Usage Examples:

The method of least squares is a standard approach in regression analysis to approximate the solution of overdetermined systems (sets of equations in.

In statistics, ordinary least squares (OLS) is a type of linear least squares method for estimating the unknown parameters in a linear regression model.

Linear least squares methods include mainly: Ordinary least squares Weighted least squares Generalized least squares Maximum likelihood estimation.

Linear least squares (LLS) is the least squares approximation of linear functions to data.

most notably limited information maximum likelihood and two-stage least squares.

For example, the method of ordinary least squares computes the unique line (or hyperplane) that minimizes the sum of squared.

In statistics, generalized least squares (GLS) is a technique for estimating the unknown parameters in a linear regression model when there is a certain.

Certain widely used methods of regression, such as ordinary least squares, have favourable properties if their underlying assumptions are true.

Non-linear least squares is the form of least squares analysis used to fit a set of m observations with a model that is non-linear in n unknown parameters.

Partial least squares regression (PLS regression) is a statistical method that bears some relation to principal components regression; instead of finding.

It is common to make the additional stipulation that the ordinary least squares (OLS) method should be used: the accuracy of each predicted value is.

For details concerning nonlinear data modeling see least squares and non-linear least squares.

The method of iteratively reweighted least squares (IRLS) is used to solve certain optimization problems with objective functions of the form of a p-norm:.

damped least-squares (DLS) method, is used to solve non-linear least squares problems.

These minimization problems arise especially in least squares curve.

inverse column-updating method, the quasi-Newton least squares method and the quasi-Newton inverse least squares method.

LOWESS thus build on "classical" methods, such as linear and nonlinear least squares regression.

interest is correlated with the error term, in which case ordinary least squares and ANOVA give biased results.

Weighted least squares (WLS), also known as weighted linear regression, is a generalization of ordinary least squares and linear regression in which knowledge.

Non-linear iterative partial least squares (NIPALS) is a variant the classical power iteration with matrix deflation.

In applied statistics, total least squares is a type of errors-in-variables regression, a least squares data modeling technique in which observational.

Synonyms:

statistical procedure; method of least squares; statistical method;

Antonyms:

most; inconsequence;