weighted least square regression

A technique for correcting the problem of heteroskedasticity by log-likelihood estimation of a weight that adjusts the errors of prediction

if some assumptions of ordinarly least square OLS regression do not hold.
.... in case of heteroscedasticity
.....unequal precision/reliability of datapoints

1. san Houston Uni - Weighted least squares regression XXX
http://www.shsu.edu/~icc_cmf/cj_789/weightedLeastSquares2.doc
- very good and detailed review of the technique, from the description of situations when to use WLS /ie if violation of homoscedasticity/ to detailed and very clear examples how to perform it /mainly applies to SPSS, the output is provided/.
- discusses both approaches on calculating weights 1/residualising the response variable 2/log-likelihood estimation of weights
- includes some basic mathematics, mostly well comprehensible college-level, formulas perfectly support the statements in text
/dr Charles M. Friel - author of this text provides lecture notes to a wide selection of statistical topics, comparable to Garson's Statnotes, in some topic I found dr Friel's explanations more poignant.
http://www.shsu.edu/~icc_cmf/directory.htm

2. statnotes - garson -including exemplification on SPSS ooutput - insightful, but not much regarding the mechanics of weight estimation / but maybe not so important/.

3. NIST Handbook - nice figures, comparison of WLS with alternative of nonlinear transformation of response and predictor variables.