Description: Nested models are statistical structures that are hierarchically related to each other, allowing the inclusion of variables at different levels of complexity. In predictive analysis, these models are fundamental for understanding how variables influence a specific outcome, facilitating the comparison between simpler and more complex models. The main characteristic of nested models is that one can be considered a special case of the other, allowing for the evaluation of improvements in predictive capability by adding additional variables. This hierarchical relationship is particularly useful in contexts where grouped or hierarchical data is analyzed, such as in studies of social sciences, healthcare, or market research. Additionally, nested models allow for hypothesis testing to determine if the inclusion of additional variables significantly improves the model fit. In summary, nested models are powerful tools in predictive analysis, as they provide a structured way to explore and validate the relationship between variables in complex contexts.
