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  2. Residue (complex analysis) - Wikipedia

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

    This formula can be very useful in determining the residues for low-order poles. For higher-order poles, the calculations can become unmanageable, and series expansion is usually easier. For essential singularities, no such simple formula exists, and residues must usually be taken directly from series expansions.

  3. 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 ...

  4. 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.

  5. 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 ...

  6. 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.

  7. Simple linear regression - Wikipedia

    en.wikipedia.org/wiki/Simple_linear_regression

    As mentioned in the introduction, in this article the "best" fit will be understood as in the least-squares approach: a line that minimizes the sum of squared residuals (see also Errors and residuals) ^ (differences between actual and predicted values of the dependent variable y), each of which is given by, for any candidate parameter values and ,

  8. What Is Residual Value When Leasing a Car? Plus How to ... - AOL

    www.aol.com/residual-value-leasing-car-plus...

    Lessors calculate residual values using many factors, typically beginning with the vehicle's market value for the term and mileage required, but the calculation can get complex quickly.

  9. Residual risk - Wikipedia

    en.wikipedia.org/wiki/Residual_risk

    The residual risk is the amount of risk or danger associated with an action or event remaining after natural or inherent risks have been reduced by risk controls. [ 1 ] The general formula to calculate residual risk is