Salīdzināt metodes

	Ordinālā loģistikās regresijas modelis (proporcionālo izredžu modelis)	Parastā mazāko kvadrātu (OLS) regresija
Nozare	Statistika	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	2010	2019
Autors	Agresti (textbook treatment); proportional odds model	Wooldridge (textbook treatment); classical least squares
Tips	Ordinal logistic regression	Linear regression
Pirmavots	Agresti, A. (2010). Analysis of Ordinal Categorical Data (2nd ed.). Wiley.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Citi nosaukumi	proportional odds model, ordered logit, ordinal logistic regression, Ordinal Regresyon (Proportional Odds)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Saistītās	5	5
Kopsavilkums	Ordinal logistic regression models an ordered categorical outcome — such as a Likert rating, a satisfaction level, or an education tier — as a function of predictors. It is the ordinal extension of logistic regression, developed in standard treatments such as Agresti's Analysis of Ordinal Categorical Data (2010), and in its most common form it is the proportional odds model.	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).