A statistical model is a mathematical representation of a real-world phenomenon or system. It is used to describe and analyze relationships between variables and make predictions or inferences about the data.
A statistical model is typically more focused on understanding the underlying relationships and patterns in the data, while a machine learning model is more focused on making accurate predictions. Statistical models often require more assumptions and have a more interpretable structure, while machine learning models can be more complex and less interpretable.
The purpose of explaining a statistical model is to help others understand the underlying assumptions, relationships, and limitations of the model. This can aid in the interpretation and use of the model's results, as well as identifying potential issues or biases.
The performance of a statistical model can be evaluated using various metrics such as accuracy, precision, recall, and F1 score. It can also be compared to other models using techniques like cross-validation or hypothesis testing.
Yes, statistical models can be used for causal inference by controlling for confounding variables and using techniques like regression analysis or propensity score matching. However, it is important to note that establishing causality is difficult and often requires additional evidence beyond statistical modeling.