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Keywords

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CHISQ

Set this keyword to a named variable that will contain the Chi-square error statistic as the sum of squared errors between *
yi*
and A + B*
xi*
. If individual standard deviations are supplied, then the Chi-square error statistic is computed as the sum of squared errors divided by the standard deviations.

####
DOUBLE

Set this keyword to force the computation to be done in double-precision arithmetic.

####
PROB

Set this keyword to a named variable that will contain the probability that the computed fit would have a value of CHISQ or greater. If PROB is greater than 0.1, the model parameters are "believable". If PROB is less than 0.1, the accuracy of the model parameters is questionable.

####
SDEV

An *
n*
-element integer, single-, or double-precision floating-point vector that specifies the individual standard deviations for {*
xi*
, *
yi*
} used for weighting, where the weight is defined as 1/SDEV^{
2}
. If SDEV is not set, no weighting is used.

####
SIGMA

Set this keyword to a named variable that will contain a two-element vector of probable uncertainties for the model parameters.

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Example

Define two *
n*
-element vectors of paired data.

X = [-3.20, 4.49, -1.66, 0.64, -2.43, -0.89, -0.12, 1.41, $

2.95, 2.18, 3.72, 5.26]

Y = [-7.14, -1.30, -4.26, -1.90, -6.19, -3.98, -2.87, -1.66, $

-0.78, -2.61, 0.31, 1.74]

Define an *
n*
-element vector of standard deviations with a constant value of 0.85

sdev = REPLICATE(0.85, N_ELEMENTS(X))

Compute the model parameters, A and B.

PRINT, LINFIT(X, Y, SDEV=sdev)

IDL prints:

[-3.44596, 0.867329]