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Calculates the linear regression coefficient between two series where the dependent (y) variable's values are weighted by some measure of uncertainty (typically, standard deviations) such that the Chi-square goodness-of-fit is minimized.

Available in version 6.5.0 and later.


load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/contributed.ncl"  ; This library is automatically loaded
                                                             ; from NCL V6.2.0 onward.
                                                             ; No need for user to explicitly load.

	function regline_weight (
		x   [*] : numeric,  
		y   [*] : numeric,  
		yu      : numeric,  
		opt [1] : integer   

	return_val [1] :  float or double



One-dimensional arrays of the same length. Missing values should be indicated by x@_FillValue and y@_FillValue. If x@_FillValue or y@_FillValue are not set, then the NCL default (appropriate to the type of x and y) will be assumed.

Return value

The return value is a scalar of type double if x, y or yu are double, and float otherwise. Some attributes are returned as well. See the description below.


<regline_weight computes the information needed to construct a regression line where each value of y may have an uncertainty/error estimate. Most commonly, this is the standard deviation. Internally, the yu are converted to weights such that the variance of the weights is [1/yu^2]

regline_weight also returns the following attributes:

std_rc (scalar, float or double, depending on x and y)
standard error of the regression coefficient
std_yi (scalar, float or double, depending on x and y)
standard error of the y-intercept
resid (float or double)
residuals from fitted regression line
chi2 (float or double)
Chi-square goodness of fit.
nptxy (scalar, integer)
number of points used

This function is a translation of the following Fortran-77 code:

   Author:  Donald G. Luttermoser, ETSU/Physics, 2 October 2013.
   History: Based on the FIT subroutine of Numerical Recipes for FORTRAN 77

See Also

regline, regline_stats, regCoef, regCoef_n, reg_multlin_stats, equiv_sample_size, rtest


Example 1 The following example is based upon data from:

    Statistical Theory and Methodology
    J Wiley 1965   pgs: 342-346     QA276  .B77
This is the same data as Example 1 for regline except bogus measurement standard deviation (ie: uncertainties/errors) have been created for illustration. As noted in the above documentation, internally, the yu are converted to weights=[1/yu^2] where the variance of the weights is one.
      x    = (/ 1190.,1455.,1550.,1730.,1745.,1770. \
             ,  1900.,1920.,1960.,2295.,2335.,2490. \
             ,  2720.,2710.,2530.,2900.,2760.,3010. /)
      y    = (/ 1115.,1425.,1515.,1795.,1715.,1710. \
             ,  1830.,1920.,1970.,2300.,2280.,2520. \
             ,  2630.,2740.,2390.,2800.,2630.,2970. /)
      nxy  = dimsizes(y)  

    ; create *bogus* uncertainties

      YSTD = stddev(y)
      yu   = random_uniform(-0.2*YSTD, 0.3*YSTD, nxy)    ; yu[*]
      rcw  = regline_weight(x,y,yu,1) ; opt=1
The output yields:

      Variable: RC_ER
      Type: float
      Total Size: 4 bytes
                  1 values
      Number of Dimensions: 1
      Dimensions and sizes:	[1]
      Number Of Attributes: 7
        yintercept :	-69.74747
        residuals :	
        std_yi :	24.64047       ; standard error of y-intercept
        std_rc :	0.0119971      ; standard error of the regression coefficient
        chi2 :	        0.8846564      ; goodness-of-fit statistic
        p_value :	2.455325e-08   ; statistical p-value
        nxy :	18
      (0)	1.026713        ; compare with Example 1 for regline