Types of R squared
There are different types of (R2) that can be used in various purposes . The most common types are :-
- Coefficient of Determination (R2): This is the standard (R2) used in linear regression, representing the proportion of the variance in the dependent variable that is explained by the independent variables.
- Adjusted (R2): Adjusted (R2) is a modification of the standard(R2) that represent the inclusion of irrelevant predictors in a regression model. It accounts for the number of predictors in the model, providing a more accurate reflection of the model’s goodness of fit.
- Weighted (R2): In some cases, each data point may have a different weight. Weighted (R2) considers these weights when calculating the goodness of fit.
- Bayesian (R2): In Bayesian statistics, (R2) can have a Bayesian interpretation, accounting for uncertainty in the parameter estimates.
Good R Squared Value in R
In the world of numbers and models, the R-squared value plays a key role in telling us how well our models fit the data. In R Programming Language this article is a quick guide to why a solid R-squared matters and how it helps us understand if our models are doing a good job.