What does a treatment known as statistical regression assume?

In the context of statistical regression, the assumption that all examined variables are continuous reflects a critical aspect of regression analysis. Statistical regression is often used to determine the relationship between one or more independent variables and a dependent variable. When the predictor variables are considered continuous, it allows for the application of various statistical techniques that quantify the strength and nature of the relationships, as well as predict outcomes based on these variables.

Continuous variables can take on an infinite number of values within a given range, which is essential for techniques like linear regression, where the relationship between variables is represented by a line. This assumption enables researchers to utilize standard regression methodologies and perform analyses that assume linearity and normality in their data.

In contrast, when dealing with categorical variables, different methods, such as logistic regression or ANOVA, are employed. Thus, the assumption that all examined variables are continuous is fundamental to the application and interpretation of traditional regression techniques.