# regline_weight

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.*

## Prototype

load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/contributed.ncl" ; This library isautomatically 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

## Arguments

*x*

*y*

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.

## Description

<**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 onxandy)- standard error of the regression coefficient
std_yi(scalar, float or double, depending onxandy)- 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 77fit.txt

## See Also

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

## Examples

**Example 1**
The following example is based upon data from:

Brownlee Statistical Theory and Methodology J Wiley 1965 pgs: 342-346 QA276 .B77This 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 =The output yields:dimsizes(y) ; create*bogus* uncertaintiesYSTD =stddev(y) yu =random_uniform(-0.2*YSTD, 0.3*YSTD, nxy) ; yu[*] rcw =regline_weight(x,y,yu,1) ;opt=1

Variable: RC_ER Type: float Total Size: 4 bytes 1 values Number of Dimensions: 1 Dimensions and sizes: [1] Coordinates: 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 forregline