Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Regresheni ya Mstari wa Vigeu Vingi (Multivariate Multiple Linear Regression)× | Urejeshaji wa Njia ya Viwango Vidogo vya Kawaida (OLS)× | |
|---|---|---|
| Nyanja≠ | Takwimu | Ekonometriki |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 2007 | 2019 |
| Mwanzilishi≠ | Johnson & Wichern (textbook treatment); classical multivariate least squares | Wooldridge (textbook treatment); classical least squares |
| Aina≠ | Multivariate linear regression | Linear regression |
| Chanzo asilia≠ | 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 |
| Majina mbadala | 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 |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | 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). |
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