পদ্ধতির তুলনা করুন

	লজিস্টিক রিগ্রেশন	মাল্টিভেরিয়েট অ্যানালাইসিস অফ কোভেরিয়েন্স (MANCOVA)	সাধারণ ন্যূনতম বর্গক্ষেত্র (OLS) রিগ্রেশন
ক্ষেত্র	গবেষণা পরিসংখ্যান	পরিসংখ্যান	অর্থমিতি
পরিবার	Process / pipeline	Hypothesis test	Regression model
উদ্ভবের বছর	1958	1970	2019
প্রবর্তক	David Roxbee Cox	Extension of MANOVA and ANCOVA traditions; consolidated in multivariate textbooks by the 1970s–1980s	Wooldridge (textbook treatment); classical least squares
ধরন	Method	Parametric multivariate mean comparison with covariate control	Linear regression
মৌলিক উৎস	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Tabachnick, B. G. & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
অপর নাম	logit model, binomial logistic regression, LR	MANCOVA, multivariate ANCOVA, MANOVA with covariates, MANCOVA — Çok Değişkenli Kovaryans Analizi	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
সম্পর্কিত	3	5	5
সারসংক্ষেপ	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	MANCOVA (Multivariate Analysis of Covariance) is a parametric hypothesis test that simultaneously compares two or more groups on multiple continuous dependent variables while statistically controlling for one or more covariates. It extends MANOVA by incorporating covariate adjustment, a tradition consolidated in multivariate statistical methodology by the 1970s and authoritatively documented by Tabachnick and Fidell (2019).	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).