Salīdzināt metodes

	Daudzvariāciju daudzkārtējā lineārā regresija	Hotellinga T² tests	Parastā mazāko kvadrātu (OLS) regresija
Nozare	Statistika	Statistika	Ekonometrija
Saime	Regression model	Hypothesis test	Regression model
Izcelsmes gads	2007	1931	2019
Autors	Johnson & Wichern (textbook treatment); classical multivariate least squares	Harold Hotelling	Wooldridge (textbook treatment); classical least squares
Tips	Multivariate linear regression	Multivariate parametric mean comparison	Linear regression
Pirmavots	Johnson, R. A. & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis (6th ed.). Pearson. ISBN: 978-0131877153	Hotelling, H. (1931). The Generalization of Student's Ratio. Annals of Mathematical Statistics, 2(3), 360–378.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Citi nosaukumi	multivariate multiple regression, MLR with multiple dependent variables, multiple-outcome regression, Çok Değişkenli Regresyon (MLR — Çoklu DV)	Hotelling T² Testi — Çok Değişkenli t-Testi, multivariate t-test, Hotelling T-squared	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Saistītās	5	6	5
Kopsavilkums	Multivariate regression is a linear regression method that predicts several continuous dependent variables at the same time from a shared set of predictors. As developed in standard treatments such as Johnson and Wichern's Applied Multivariate Statistical Analysis (2007), each response equation can be fitted by ordinary least squares while the covariance structure of the residuals is used for joint testing across outcomes.	Hotelling's T² test is a multivariate parametric hypothesis test that simultaneously compares the mean vectors of two independent groups across multiple continuous outcome variables. It was introduced by Harold Hotelling in 1931 as the direct multivariate generalization of Student's t-test, replacing the scalar mean difference with a vector difference scaled by the pooled variance-covariance matrix.	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).