There is broad consensus that logistic regression is superior to ordinary least squares (OLS) regression at predicting the probability of an event. However, OLS is still widely used in binary choice models, mainly because OLS coefficients are more intuitive than logistic coefficients. This paper shows a simple way of calculating linear probability coefficients (LPC), similar in nature to OLS coefficients, from logistic coefficients. It also shows that OLS coefficients tend to be very close to logistic LPC coefficients
This note formalizes bias and inconsistency results for ordinary least squares (OLS) on the linear p...
The analysis of binary response data commonly uses models linear in the logistic transform of probab...
The logistic regression originally is intended to explain the relationship between the probability o...
There is broad consensus that logistic regression is superior to ordinary least squares (OLS) regres...
There is broad consensus that logistic regression is superior to ordinary least squares (OLS) regres...
There is broad consensus that logistic regression is superior to ordinary least squares (OLS) regres...
There is broad consensus that logistic regression is superior to ordinary least squares (OLS) regres...
The analysis of binary response data commonly uses models linear in the logistic transform of probab...
Linear Probability Model (LPM) is commonly used because it is easy to compute and interpret than wit...
Linear regression is among the most popular statistical models in social sciences research, and rese...
The purpose of an analysis using this method is the same as that of any technique in constructing mo...
Properties of various types of estimators of the regression coefficients in linear logistic regressi...
The conditions under which ordinary least squares (OLS) is an unbiased and consistent estimator of t...
Coefficients from logistic regression are affected by noncollapsibility, which means that the compa...
Coefficients from logistic regression are affected by noncollapsibility, which means that the compa...
This note formalizes bias and inconsistency results for ordinary least squares (OLS) on the linear p...
The analysis of binary response data commonly uses models linear in the logistic transform of probab...
The logistic regression originally is intended to explain the relationship between the probability o...
There is broad consensus that logistic regression is superior to ordinary least squares (OLS) regres...
There is broad consensus that logistic regression is superior to ordinary least squares (OLS) regres...
There is broad consensus that logistic regression is superior to ordinary least squares (OLS) regres...
There is broad consensus that logistic regression is superior to ordinary least squares (OLS) regres...
The analysis of binary response data commonly uses models linear in the logistic transform of probab...
Linear Probability Model (LPM) is commonly used because it is easy to compute and interpret than wit...
Linear regression is among the most popular statistical models in social sciences research, and rese...
The purpose of an analysis using this method is the same as that of any technique in constructing mo...
Properties of various types of estimators of the regression coefficients in linear logistic regressi...
The conditions under which ordinary least squares (OLS) is an unbiased and consistent estimator of t...
Coefficients from logistic regression are affected by noncollapsibility, which means that the compa...
Coefficients from logistic regression are affected by noncollapsibility, which means that the compa...
This note formalizes bias and inconsistency results for ordinary least squares (OLS) on the linear p...
The analysis of binary response data commonly uses models linear in the logistic transform of probab...
The logistic regression originally is intended to explain the relationship between the probability o...