Description: A probit model is a type of regression used to model binary outcome variables, that is, those that can only take two values, such as ‘yes’ or ‘no’, ‘success’ or ‘failure’. This model is based on the cumulative distribution function of the normal distribution, allowing the estimation of the probability of an event occurring based on one or more independent variables. Unlike logistic regression, which uses the logistic function, the probit model transforms the dependent variable through the normal function, which may be more suitable in certain statistical contexts. The main characteristics of the probit model include its ability to handle binary data and its interpretation in terms of probabilities. Additionally, it is particularly useful when it is assumed that errors in the model follow a normal distribution. This approach is widely used in various disciplines, such as economics, biology, and social sciences, where it is necessary to analyze binary decisions or discrete events. The estimation of the model parameters is commonly performed using the maximum likelihood method, which allows for robust and reliable results in predicting probabilities of binary events.
History: The probit model was developed in the 1930s by American economist Chester Ittner Bliss, who introduced it as a way to analyze binary data in the context of agricultural research. Over the years, the model has evolved and become more accessible thanks to the development of statistical software that allows its implementation in various research areas.
Uses: The probit model is primarily used in economics to analyze consumption decisions, in medicine to study the effectiveness of treatments, and in social sciences to investigate behaviors and choices. It is also applied in marketing studies to predict the likelihood of a consumer choosing one product over another.
Examples: A practical example of using the probit model is in a study analyzing the likelihood of a patient adhering to a medical treatment based on variables such as age, gender, and income level. Another example is in market research, where it can be used to predict whether a customer will purchase a product based on demographic characteristics and preferences.
