Abstract
In this research weights, which are used, are estimated using General Least Square Estimation to estimate simple linear regression parameters when the depended variable, which is used, consists of two classes attributes variable (for Heteroscedastic problem) depending on Sequential Bayesian Approach instead of the Classical approach used before, Bayes approach provides the mechanism of tackling observations one by one in a sequential way, i .e each new observation will add a new piece of information for estimating the parameter of probability estimation of certain phenomenon of Bernoulli trials who research the depended variable in simple regression linear equation. in addition to the information deduced from the past experiences or self dependence. the research also contains a comparison between both approaches using practical application of both approaches for estimating the simple linear regression between the income and the state of having a house living in for the official in college of Administration and Economics in Salah-Alden University/Erbil .
DOI
10.33095/jeas.v16i60.1523
Subject Area
Statistical
First Page
216
Last Page
227
Recommended Citation
Ali, T. H., & Hassan Chawsheen, T. A. (2010). Comparative Study of Regression Parameter Estimation Methods Under Heteroscedasticity with Application. Journal of Economics and Administrative Sciences, 16(60), 216-227. https://doi.org/10.33095/jeas.v16i60.1523
