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| Regressió per Mínims Quadrats Ordinàris (MQO)× | Regressió Logística× | |
|---|---|---|
| Camp≠ | Econometria | Estadística per a la recerca |
| Família≠ | Regression model | Process / pipeline |
| Any d'origen≠ | 2019 | 1958 |
| Autor original≠ | Wooldridge (textbook treatment); classical least squares | David Roxbee Cox |
| Tipus≠ | Linear regression | Method |
| Font seminal≠ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Àlies≠ | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | logit model, binomial logistic regression, LR |
| Relacionats≠ | 5 | 3 |
| Resum≠ | 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). | 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. |
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