পদ্ধতির তুলনা করুন

	প্রোবিট রিগ্রেশন মডেল	সাধারণ ন্যূনতম বর্গক্ষেত্র (OLS) রিগ্রেশন	প্যানেল ডেটা ফিক্সড এফেক্টস মডেল
ক্ষেত্র	অর্থমিতি	অর্থমিতি	অর্থমিতি
পরিবার	Regression model	Regression model	Regression model
উদ্ভবের বছর	2018	2019	2014
প্রবর্তক	Greene (textbook treatment); classical discrete-choice modelling	Wooldridge (textbook treatment); classical least squares	Hsiao (textbook treatment); within transformation of panel data
ধরন	Binary discrete-choice model	Linear regression	Panel data regression
মৌলিক উৎস	Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson. ISBN: 978-0134461366	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
অপর নাম	probit regression, normit model, Probit Modeli	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
সম্পর্কিত	5	5	5
সারসংক্ষেপ	The probit model is a regression method for a binary (0/1) outcome that maps a linear index of the predictors through the standard normal cumulative distribution function to produce a probability. It is a classical discrete-choice alternative to logistic regression, developed in standard econometrics treatments such as Greene's Econometric Analysis (2018).	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).	The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014).