পদ্ধতির তুলনা করুন

	Weighted Least Squares (WLS)	হেটেরোস্কেডাস্টিসিটির জন্য হোয়াইট পরীক্ষা
ক্ষেত্র	পরিসংখ্যান	অর্থমিতি
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1935	1980
প্রবর্তক	Alexander Craig Aitken	Halbert White
ধরন	Weighted linear estimator	General test for heteroskedasticity
মৌলিক উৎস	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.
অপর নাম	WLS, weighted regression, heteroscedasticity-corrected OLS, variance-weighted least squares	White's general heteroskedasticity test, White değişen varyans testi
সম্পর্কিত	3	3
সারসংক্ষেপ	Weighted Least Squares is a generalization of Ordinary Least Squares (OLS) regression that assigns each observation a weight inversely proportional to its error variance, thereby down-weighting high-variance data points and up-weighting precise ones. Introduced in its general matrix form by Alexander Craig Aitken in 1935, WLS is the canonical remedy when heteroscedasticity is present and the error variance structure is known or can be reliably estimated.	The White test, introduced by Halbert White in 1980, is a general test for heteroskedasticity that makes no assumption about its functional form. It regresses the squared OLS residuals on the regressors, their squares, and their cross-products, so it can detect heteroskedasticity related to any of these terms. The same 1980 paper introduced the heteroskedasticity-consistent ('White') standard errors that are the standard remedy when the test rejects.