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2. Regression analysis - Wikipedia

   en.wikipedia.org/wiki/Regression_analysis

   In linear regression, the model specification is that the dependent variable, is a linear combination of the parameters (but need not be linear in the independent variables). For example, in simple linear regression for modeling n {\displaystyle n} data points there is one independent variable: x i {\displaystyle x_{i}} , and two parameters, β ...

3. Coefficient of determination - Wikipedia

   en.wikipedia.org/wiki/Coefficient_of_determination

   Ordinary least squares regression of Okun's law.Since the regression line does not miss any of the points by very much, the R 2 of the regression is relatively high.. In statistics, the coefficient of determination, denoted R 2 or r 2 and pronounced "R squared", is the proportion of the variation in the dependent variable that is predictable from the independent variable(s).

4. Linear regression - Wikipedia

   en.wikipedia.org/wiki/Linear_regression

   In statistics, linear regression is a model that estimates the linear relationship between a ... that the predictor variables are assumed to be error-free—that is ...

5. 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 ...

6. 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 ...

7. 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 ...