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2. Linear regression - Wikipedia

   en.wikipedia.org/wiki/Linear_regression

   A model with exactly one explanatory variable is a simple linear regression; a model with two or more explanatory variables is a multiple linear regression. [1] This term is distinct from multivariate linear regression , which predicts multiple correlated dependent variables rather than a single dependent variable.

3. File:A First Course in Linear Algebra for print.pdf - Wikipedia

   en.wikipedia.org/wiki/File:A_First_Course_in...

   Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts

4. Simple linear regression - Wikipedia

   en.wikipedia.org/wiki/Simple_linear_regression

   Deming regression (total least squares) also finds a line that fits a set of two-dimensional sample points, but (unlike ordinary least squares, least absolute deviations, and median slope regression) it is not really an instance of simple linear regression, because it does not separate the coordinates into one dependent and one independent ...

5. Leverage (statistics) - Wikipedia

   en.wikipedia.org/wiki/Leverage_(statistics)

   Note that the partial leverage is the leverage of the point in the partial regression plot for the variable. Data points with large partial leverage for an independent variable can exert undue influence on the selection of that variable in automatic regression model building procedures.

6. Linear least squares - Wikipedia

   en.wikipedia.org/wiki/Linear_least_squares

   It is one approach to handling the "errors in variables" problem, and is also sometimes used even when the covariates are assumed to be error-free. Linear Template Fit (LTF) [7] combines a linear regression with (generalized) least squares in order to determine the best estimator. The Linear Template Fit addresses the frequent issue, when the ...

7. Ordinary least squares - Wikipedia

   en.wikipedia.org/wiki/Ordinary_least_squares

   In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one [clarification needed] effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values ...