AIC
- Created by
- Renato Passos, Eng. de Software
- Reviewed by
- Renato Passos, Eng. de Software
Last updated: Apr 18, 2026
Formula
AIC
About this calculator
The AIC Calculator is a useful tool for statisticians and data scientists who want to evaluate the quality of regression models.
It calculates the AIC (Akaike Information Criterion) using the formula AIC = 2k − 2·ln(L), where k is the number of parameters in the model and L is the likelihood of the data.
The AIC is an important measure to compare different models and choose the best one for a given dataset. For example, if you are working with a simple and complex linear regression model, you can use the calculator to compare their AIC and choose the best fit.
Also, it's essential to be careful when interpreting the AIC, as it can be affected by factors such as model choice and data distribution.
Frequently asked questions
What is AIC?
AIC is a measure of model quality that helps to compare different fits and choose the best one for a given dataset.
When should I use the AIC calculator?
You should use it when you need to compare the fits of different regression models and choose the best one for your data.
How do I interpret AIC?
It's essential to consider the model choice and data distribution when interpreting AIC.
What is k in AIC = 2k − 2·ln(L)?
k is the number of parameters in the model, or the number of independent variables used in the regression model.
What is the likelihood of the data (L)?
The likelihood of the data (L) is the probability that the data are verified by the model.