Comparar métodos

	Regressão Múltipla Linear Multivariada	Regressão por Mínimos Quadrados Ordinários (MQO)
Área	Estatística	Econometria
Família	Regression model	Regression model
Ano de origem	2007	2019
Autor original	Johnson & Wichern (textbook treatment); classical multivariate least squares	Wooldridge (textbook treatment); classical least squares
Tipo	Multivariate linear regression	Linear regression
Fonte seminal	Johnson, R. A. & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis (6th ed.). Pearson. ISBN: 978-0131877153	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Outros nomes	multivariate multiple regression, MLR with multiple dependent variables, multiple-outcome regression, Çok Değişkenli Regresyon (MLR — Çoklu DV)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Relacionados	5	5
Resumo	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.	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).