পদ্ধতির তুলনা করুন

	সাধারণ ন্যূনতম বর্গক্ষেত্র (OLS) রিগ্রেশন	ভেদাঙ্ক স্ফীতি গুণাঙ্ক (Variance Inflation Factor - VIF)
ক্ষেত্র	অর্থমিতি	অর্থমিতি
পরিবার	Regression model	Regression model
উদ্ভবের বছর	2019	1970
প্রবর্তক	Wooldridge (textbook treatment); classical least squares	Donald Marquardt
ধরন	Linear regression	Diagnostic statistic
মৌলিক উৎস	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Marquardt, D. W. (1970). Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation. Technometrics, 12(3), 591–612.
অপর নাম	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	VIF, Variance Inflation Index, Multicollinearity Inflation Factor, Varyans Enflasyon Faktörü
সম্পর্কিত	5	3
সারসংক্ষেপ	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	The Variance Inflation Factor (VIF) is a scalar diagnostic statistic proposed by Donald Marquardt (1970) that quantifies how much the variance of an estimated regression coefficient increases due to linear dependence—multicollinearity—among the predictors in an ordinary least squares model. It is routinely applied in econometrics, social science, and biomedical research whenever analysts suspect that two or more independent variables move together closely enough to destabilize coefficient estimates.