method of least square वाक्य
उदाहरण वाक्य
मोबाइल
- Polynomial regression models are usually fit using the method of least squares.
- Alignment measurement method of least squares; 2-1.
- The method of least squares was first described by Adrien-Marie Legendre in 1805.
- By the method of least squares.
- He is chiefly remembered for his formulation of the method of least squares, published in 1808.
- Because of the nature of the method of least squares, the whole vector of residuals, with
- The first clear and concise exposition of the method of least squares was published by Legendre in 1805.
- In that work he claimed to have been in possession of the method of least squares since 1795.
- The mathematical forms arising from quantile regression are distinct from those arising in the method of least squares.
- In practice multiple arc measurements are used to determine the ellipsoid parameters by the method of least squares.
- This makes it possible to find optimal coefficients through the method of least squares using simple matrix operations.
- In evaluating the SiTF curve, the signal input and signal output are measured the method of least squares.
- If it is important to give greater weight to outliers, the method of least squares is a better choice.
- The value of Legendre's method of least squares was immediately recognized by leading astronomers and geodesists of the time.
- In the history of statistics, this disagreement is called the " priority dispute over the discovery of the method of least squares ."
- However, once there, he began studying mathematics under Carl Friedrich Gauss ( specifically his lectures on the method of least squares ).
- The values of a and b, obtained by the method of least squares, are 3.60 and 0.0624 respectively.
- The doses z are transformed to logarithms x = \ log ( z ) and the method of least squares is used to fit two parallel lines to the data.
- The following is a table contrasting some properties of the method of least absolute deviations with those of the method of least squares ( for non-singular problems ).
- However, to Gauss's credit, he went beyond Legendre and succeeded in connecting the method of least squares with the principles of probability and to the normal distribution.
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