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  2. 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).

  3. Linear trend estimation - Wikipedia

    en.wikipedia.org/wiki/Linear_trend_estimation

    All have the same trend, but more filtering leads to higher r 2 of fitted trend line. The least-squares fitting process produces a value, r-squared (r 2), which is 1 minus the ratio of the variance of the residuals to the variance of the dependent variable. It says what fraction of the variance of the data is explained by the fitted trend line.

  4. Linear least squares - Wikipedia

    en.wikipedia.org/wiki/Linear_least_squares

    Linear least squares (LLS) is the least squares approximation of linear functions to data. It is a set of formulations for solving statistical problems involved in linear regression, including variants for ordinary (unweighted), weighted, and generalized (correlated) residuals.

  5. Talk:Coefficient of determination - Wikipedia

    en.wikipedia.org/wiki/Talk:Coefficient_of...

    I believe that R-squared is a measure of variability aligned rather than variability accounted for. With respect to correlation is not causation, consider R-squared as variabilty "aligned" rather than "accounted for." For example, if the number of churches in cities is correlated with the number of bars in cites, say .9 , then R-squared is .81.

  6. R-square - Wikipedia

    en.wikipedia.org/?title=R-square&redirect=no

    This page was last edited on 12 June 2006, at 01:51 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may ...

  7. Regularized least squares - Wikipedia

    en.wikipedia.org/wiki/Regularized_least_squares

    The first term is the objective function from ordinary least squares (OLS) regression, corresponding to the residual sum of squares. The second term is a regularization term, not present in OLS, which penalizes large values. As a smooth finite dimensional problem is considered and it is possible to apply standard calculus tools.

  8. Numerical methods for linear least squares - Wikipedia

    en.wikipedia.org/wiki/Numerical_methods_for...

    The equation = is known as the normal equation. The algebraic solution of the normal equations with a full-rank matrix X T X can be written as ^ = = + where X + is the Moore–Penrose pseudoinverse of X.

  9. Exercise (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Exercise_(mathematics)

    It was widely believed in Cambridge that the best way of teaching mathematics, including the new analytical methods, was through practical examples and problems, and, by the mid-1830s, some of the first generation of young college fellows to have been taught higher analysis this way were beginning both to undertake their own research and to be ...

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