least square method sentence in Hindi
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- Other methods that can be used are the Column Updating Method, the Inverse Column Updating Method, the Quasi-Newton Least Squares Method and the Quasi-Newton Inverse Least Squares Method.
- Other methods that can be used are the Column Updating Method, the Inverse Column Updating Method, the Quasi-Newton Least Squares Method and the Quasi-Newton Inverse Least Squares Method.
- When both steric and polar effects influence the reaction rate the Taft equation can be solved for both ? * and ? through the use of standard least squares methods for determining a bivariant regression plane.
- Two common applications of polynomial least squares methods are approximating a low-degree polynomial that approximates a complicated function and estimating an assumed underlying polynomial from corrupted ( also known as " noisy " ) observations.
- Many common statistics, including t-tests, regression models, design of experiments, and much else, use least squares methods applied using linear regression theory, which is based on the quadratric loss function.
- :: I think I understand linear least squares method but in my understanding one requies a set of x and y data points from which the matrix and its inverse are derived as well as the vector.
- This is applied, e . g ., in the Kalman filter and recursive least squares methods, to replace the parametric solution, requiring inversion of a state vector sized matrix, with a condition equations based solution.
- Classical objective functions include the sum of squared deviations between experimental and numerical data, as in the least squares methods, the sum of the magnitude of the difference between field and numerical data, or some variant of these definitions.
- :: If you want to go one step further and draw a straight line that isn't parallel to the axis, you could use a sum of least squares method or some other method to fit a straight line to the date.
- Another approach for solving fluid dynamic equations in a grid free framework is the moving least squares or least squares method ( Belytschko et al . 1996, Dilts 1996, Kuhnert 99, Kuhnert 2000, Tiwari et al . 2001 and 2000 ).
- Haelterman et al . also showed that when the Quasi-Newton Least Squares Method is applied to a linear system of size, it converges in at most steps although like all quasi-Newton methods, it may not converge for nonlinear systems.
- Haelterman et al . also showed that when the Quasi-Newton Inverse Least Squares Method is applied to a linear system of size, it converges in at most steps although like all quasi-Newton methods, it may not converge for nonlinear systems.
- This still leaves the question of how to obtain estimators in a given situation and carry the computation, several methods have been proposed : the method of moments, the maximum likelihood method, the least squares method and the more recent method of estimating equations.
- When the problem has substantial uncertainties in the independent variable ( the " x " variable ), then simple regression and least squares methods have problems; in such cases, the methodology required for fitting errors-in-variables models may be considered instead of that for least squares.
- A spreadsheet application of this for parabolic curves has been developed by NTS . The spreadsheet fits a parabola to 4 or more points ( up to 10 allowed ) using the least squares method and then calculates the limb length ( s ) using Simpson's Rule to evaluate the definite integral.
- In 1945 to 1965, Wold proposed and elaborated on his " recursive causal chain " model, which was more appropriate for many applications, according to Wold : For such " recursive causal chain " models, the least squares method was computationally efficient and enjoyed superior theoretical properties, which it lacked for general time-series models.
- :See System of linear equations for how to represent a collection of linear equations in the matrix vector form A "'x "'= "'b "'that is used in the description of the linear least squares method .-- Talk 09 : 29, 22 November 2006 ( UTC)
- In the next figure the break point is found at X = 7.9 while for the same data ( see blue figure above for mustard yield ), the least squares method yields a break point only at X = 4.9 . The latter value is lower, but the fit of the data beyond the break point is better.
- The minimization of "'P1 "'is solved through the conjugate gradient least squares method . "'P2 "'refers to the second step of the iterative reconstruction process wherein it utilizes the edge-preserving total variation regularization term to remove noise and artifacts, and thus improve the quality of the reconstructed image / signal.
- The least squares method applied separately to each segment, by which the two regression lines are made to fit the data set as closely as possible while minimizing the " sum of squares of the differences " ( SSD ) between observed ( "'y "') and calculated ( Yr ) values of the dependent variable, results in the following two equations:
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