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  2. Residual (numerical analysis) - Wikipedia

    en.wikipedia.org/wiki/Residual_(numerical_analysis)

    When one does not know the exact solution, one may look for the approximation with small residual. Residuals appear in many areas in mathematics, including iterative solvers such as the generalized minimal residual method, which seeks solutions to equations by systematically minimizing the residual.

  3. Residue (complex analysis) - Wikipedia

    en.wikipedia.org/wiki/Residue_(complex_analysis)

    The definition of a residue can be generalized to arbitrary Riemann surfaces. Suppose ω {\displaystyle \omega } is a 1-form on a Riemann surface. Let ω {\displaystyle \omega } be meromorphic at some point x {\displaystyle x} , so that we may write ω {\displaystyle \omega } in local coordinates as f ( z ) d z {\displaystyle f(z)\;dz} .

  4. Errors and residuals - Wikipedia

    en.wikipedia.org/wiki/Errors_and_residuals

    The difference between the height of each man in the sample and the observable sample mean is a residual. Note that, because of the definition of the sample mean, the sum of the residuals within a random sample is necessarily zero, and thus the residuals are necessarily not independent.

  5. Residue theorem - Wikipedia

    en.wikipedia.org/wiki/Residue_theorem

    In complex analysis, the residue theorem, sometimes called Cauchy's residue theorem, is a powerful tool to evaluate line integrals of analytic functions over closed curves; it can often be used to compute real integrals and infinite series as well.

  6. Residual sum of squares - Wikipedia

    en.wikipedia.org/wiki/Residual_sum_of_squares

    The general regression model with n observations and k explanators, the first of which is a constant unit vector whose coefficient is the regression intercept, is = + where y is an n × 1 vector of dependent variable observations, each column of the n × k matrix X is a vector of observations on one of the k explanators, is a k × 1 vector of true coefficients, and e is an n× 1 vector of the ...

  7. Least squares - Wikipedia

    en.wikipedia.org/wiki/Least_squares

    The result of fitting a set of data points with a quadratic function Conic fitting a set of points using least-squares approximation. In regression analysis, least squares is a parameter estimation method based on minimizing the sum of the squares of the residuals (a residual being the difference between an observed value and the fitted value provided by a model) made in the results of each ...

  8. Elle a 'perfect addition' at new forever home after 1,000 ...

    www.aol.com/elle-perfect-addition-forever-home...

    Finding a loving home Despite all the work Warmbrod and shelter staff put into Elle, it still took a while for her to find a loving home. "I think primarily because she's a bigger dog," Warmbrod said.

  9. Remainder - Wikipedia

    en.wikipedia.org/wiki/Remainder

    For algorithms describing how to calculate the remainder, see division algorithm.) The remainder, as defined above, is called the least positive remainder or simply the remainder. [2] The integer a is either a multiple of d, or lies in the interval between consecutive multiples of d, namely, q⋅d and (q + 1)d (for positive q).