Standard Error (Provided)

This is the default selection. It assumes that the random errors are heteroscedastic (have non-constant variance) and estimates the regression coefficients by weighted least squares, where weights at each point are:

• For model y = xb
  
  w = 1/v, where v is the square of the std dev that has been input for that point.

• For model ln (y) = xb
  
  w = (y²)/v, where y² is the square of the response for that point and v is the square of the std dev that has been input for that point. (Motivated by delta method.)