PROBIT REGRESSION Meaning and
Definition
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Probit regression is a statistical method used to analyze data and model the relationship between a binary or ordinal dependent variable and one or more independent variables. It is a type of regression analysis that assumes the dependent variable follows a probit distribution, which is the cumulative distribution function (CDF) of the standard normal distribution.
In probit regression, the dependent variable is typically a binary variable that can take only two values, such as "success" or "failure," "yes" or "no," or "0" or "1." However, it can also be extended to handle ordinal variables with more than two categories. The independent variables can be continuous, discrete, or categorical, and their coefficients represent the change in the odds of the dependent variable for a unit change in the independent variable.
The probit regression model estimates the parameters of the relationship between the independent variables and the probability of the dependent variable. It uses maximum likelihood estimation to find the coefficients that maximize the likelihood of observing the given data. The model produces probability estimates for the dependent variable and can be used to predict the probability of a specific outcome based on the values of the independent variables.
Probit regression finds applications in various fields, such as economics, social sciences, psychology, and medicine. It is commonly used in cases where the dependent variable is a discrete outcome that cannot be directly modelled by linear regression. By estimating the underlying probability distribution, probit regression provides a flexible and powerful approach to analyzing and predicting binary or ordinal outcomes.
Etymology of PROBIT REGRESSION
The term "probit regression" is derived from two different sources: "probit" and "regression".
"Probit" is a combination of the words "probability" and "bit". The term "probit" was coined by Chester Bliss in 1934 as he developed the probit model, a statistical technique used for analyzing data with binary or categorical outcomes. The "probit" model transforms the linear predictions of a regression model into probabilities, allowing the estimation of the relationship between predictors and a binary response variable.
"Regression" refers to the broader statistical concept of estimating the relationship between one or more independent variables and a dependent variable. In "probit regression", the term "regression" indicates that the technique involves estimating the relationship or regression function between predictors and a binary response variable using the probit model.
