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least-square distance vs least square distance

Both "least-square distance" and "least square distance" are correct, but they are used in different contexts. "Least-square distance" is more commonly used in mathematics and statistics to refer to a method of finding the best-fitting line through a set of points. On the other hand, "least square distance" could be used in a more general sense to describe the smallest distance between two points without the specific mathematical connotation.

Last updated: March 31, 2024 • 591 views

least-square distance

This phrase is correct and commonly used in mathematics and statistics.

This phrase is used in mathematics and statistics to refer to a method of finding the best-fitting line through a set of points by minimizing the sum of the squares of the vertical distances between the points and the line.
  • All exhaust emissions results shall be plotted as a function of the running distance on the system rounded to the nearest kilometre and the best fit straight line fitted by the method of least squares shall be drawn through all these data points.

Alternatives:

  • least-squares distance
  • least-squares method
  • least-squares regression
  • least-squares fitting
  • least-squares solution

least square distance

This phrase is correct but may be used in a more general sense.

This phrase could be used in a more general context to describe the smallest distance between two points without the specific mathematical connotation of the "least-square" method.
  • And inversely proportional to the square of the distance between them.
  • And it's inversely proportional... to the square of the distance between them.
  • Safety distances for square or circular apertures
  • There is some evidence to suggest the potential telepathic intensity varies indirectly as the square of the distance between two telepathists.
  • Since exposure drops with the square of the distance, lowering the safety level will increase this zone by a factor of around 2,5 to 3,5.
  • A trivial example is this: Newton found the law of gravity, which goes like one over the square of the distance between the things gravitated.
  • Exposure drops with the square of the distance, which explains the low exposure levels measured in practice.
  • The distance of measurement shall be such that the law of the inverse of the square of the distance is applicable;
  • All exhaust emissions results shall be plotted as a function of the running distance on the system rounded to the nearest kilometre and the best fit straight line fitted by the method of least squares shall be drawn through all these data points.
  • The calibration curve is calculated by the method of least squares.
  • The arithmetic mean values over the 30 s period shall be used to calculate the least squares linear regression parameters according to equation 6 in paragraph 7.7.2.
  • The least squares method shall be used for the fitting of the two curves.
  • When the model is fitted by Least Squares, a transformation should be applied to the per-vessel statistics in order to improve the homogeneity of variance.
  • Least squares means adjusted for prior antihyperglycaemic therapy status and baseline value. p < 0.001 compared to placebo or placebo + combination treatment.
  • The slope of the straight line representing the best fit to the calibration values determined by the method of least square within the channel amplitude class.
  • Least squares means adjusted for prior antihyperglycaemic therapy status and baseline value. p < 0.001 compared to placebo or placebo + combination treatment.
  • The measured values shall be collectively compared to the reference values by using a least squares linear regression and the linearity criteria specified in Table 8.2 of this paragraph.
  • A linear least-square fit is performed to generate the calibration equations which have the formulae:
  • In this case, the use of complex computer programmes is not required, if the triglyceride combinations used in Table 2 are applied and the factors redetermined by using the method of least squares.
  • The continuous line represents the linear regression, the coefficients of which are calculated by the least squares method.

Alternatives:

  • shortest distance
  • minimum distance
  • smallest distance
  • closest distance
  • minimum separation

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