Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Msawazo Mkuu wa Mlinganyo (MLR)× | Urejeshaji wa Njia ya Viwango Vidogo vya Kawaida (OLS)× | |
|---|---|---|
| Nyanja≠ | Takwimu | Ekonometriki |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 1886 | 2019 |
| Mwanzilishi≠ | Francis Galton; formalized by Karl Pearson | Wooldridge (textbook treatment); classical least squares |
| Aina≠ | Parametric linear model | Linear regression |
| Chanzo asilia≠ | Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Majina mbadala≠ | MLR, OLS regression, multiple regression, linear regression with multiple predictors | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Zinazohusiana≠ | 8 | 5 |
| Muhtasari≠ | Multiple linear regression (MLR) is a parametric regression model that expresses a continuous outcome as a weighted linear combination of two or more predictor variables plus a random error term. The unknown weights (regression coefficients) are estimated by ordinary least squares (OLS), which minimises the sum of squared residuals. The method traces to Francis Galton's 1886 work on hereditary stature and was placed on firm mathematical footing by Karl Pearson; Draper and Smith's 1966 textbook established it as the standard framework for applied regression. | 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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