The functions described in this section can be used to perform least-squares fits to a straight line model, @math{Y = c_0 + c_1 X}. For weighted data the best-fit is found by minimizing the weighted sum of squared residuals, @math{\chi^2},

for the parameters @math{c_0}, @math{c_1}. For unweighted data the sum is computed with @math{w_i = 1}.

__Function:__int**gsl_fit_linear***(const double **`x`, const size_t`xstride`, const double *`y`, const size_t`ystride`, size_t`n`, double *`c0`, double *`c1`, double *`cov00`, double *`cov01`, double *`cov11`, double *`sumsq`)-
This function computes the best-fit linear regression coefficients
(
`c0`,`c1`) of the model @math{Y = c_0 + c_1 X} for the datasets (`x`,`y`), two vectors of length`n`with strides`xstride`and`ystride`. The variance-covariance matrix for the parameters (`c0`,`c1`) is estimated from the scatter of the points around the best-fit line and returned via the parameters (`cov00`,`cov01`,`cov11`). The sum of squares of the residuals from the best-fit line is returned in`sumsq`.

__Function:__int**gsl_fit_wlinear***(const double **`x`, const size_t`xstride`, const double *`w`, const size_t`wstride`, const double *`y`, const size_t`ystride`, size_t`n`, double *`c0`, double *`c1`, double *`cov00`, double *`cov01`, double *`cov11`, double *`chisq`)-
This function computes the best-fit linear regression coefficients
(
`c0`,`c1`) of the model @math{Y = c_0 + c_1 X} for the weighted datasets (`x`,`y`), two vectors of length`n`with strides`xstride`and`ystride`. The vector`w`, of length`n`and stride`wstride`, specifies the weight of each datapoint. The weight is the reciprocal of the variance for each datapoint in`y`.The covariance matrix for the parameters (

`c0`,`c1`) is estimated from weighted data and returned via the parameters (`cov00`,`cov01`,`cov11`). The weighted sum of squares of the residuals from the best-fit line, @math{\chi^2}, is returned in`chisq`.

__Function:__int**gsl_fit_linear_est***(double*`x`, double`c0`, double`c1`, double`c00`, double`c01`, double`c11`, double *`y`, double *`y_err`)-
This function uses the best-fit linear regression coefficients
`c0`,`c1`and their estimated covariance`cov00`,`cov01`,`cov11`to compute the fitted function`y`and its standard deviation`y_err`for the model @math{Y = c_0 + c_1 X} at the point`x`.

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