Description: Bivariate logistic regression is a statistical method used to model the relationship between a binary dependent variable and one or more independent variables. This approach allows predicting the probability of a specific event occurring, such as success or failure, based on the characteristics of the independent variables. Unlike linear regression, which is used for continuous variables, logistic regression focuses on classification and predicting categorical outcomes. The logistic function, which transforms the output of linear regression into a value between 0 and 1, is fundamental in this model, as it allows interpreting the results as probabilities. Bivariate logistic regression is characterized by its simplicity and effectiveness in situations where one or more independent variables influence a binary outcome. This method is widely used in various disciplines, including medicine, economics, and social sciences, where the relationship between factors and categorical outcomes is analyzed. Its ability to handle nonlinear data and its intuitive interpretation make it a valuable tool for researchers and analysts.
History: Logistic regression was developed in the 1940s by statistician David Cox, who introduced the model in the context of biology and medicine. Over the years, its use has expanded to various disciplines, including economics and social sciences. In the 1970s, the method began to gain popularity in data analysis, especially in epidemiological and public health studies. The introduction of statistical software in the following decades facilitated its application, allowing researchers to perform more complex analyses and obtain more accurate results.
Uses: Bivariate logistic regression is used in a variety of fields, such as medicine to predict the probability of diseases based on risk factors, in marketing to analyze the likelihood of a customer purchasing a product, and in social sciences to study the relationship between demographic variables and behaviors. It is also common in public health studies to evaluate the effectiveness of interventions and in consumer behavior research.
Examples: A practical example of bivariate logistic regression is a study analyzing the relationship between tobacco use (independent variable) and lung cancer incidence (binary dependent variable: yes/no). Another example could be an analysis evaluating how education (independent variable) influences the likelihood of employment (binary dependent variable: employed/unemployed).
