What is a fitted model?

A fitted model is a statistical model that has been fitted or estimated using mathematical algorithms to provide predictions about a certain variable or relationship between variables. This model is usually developed based on a set of observed or measured data points and is used to make predictions or identify trends in new and/or future data.

Fitted models can be used in a wide range of applications, from predicting stock prices to modeling the spread of a disease. They are most commonly used in data analysis and machine learning, where they can be used to identify important patterns and relationships in large data sets, generate insights, and make predictions.

To develop a fitted model, a number of different statistical techniques can be used, including linear regression, logistic regression, time series analysis, and machine learning algorithms such as k-nearest neighbors, decision trees, and neural networks. The choice of technique will largely depend on the nature of the data and the research question being investigated.

Overall, a fitted model is a powerful tool for data analysis and prediction, enabling researchers and data analysts to identify important relationships, patterns, and trends in their data and make informed decisions based on this information.