R-squared is a goodness-of-fit measure for linear regression models. This statistic indicates the percentage of the variance in the dependent variable that the independent variables explain collectively.

In statistics, the coefficient of determination, denoted R2 or r2 and pronounced "R squared", is the proportion of the variation in the dependent variable that is predictable from the independent variable (s).

May 7, 2021 · Here’s how to interpret the R and R-squared values of this model: R: The correlation between hours studied and exam score is 0.959. R2: The R-squared for this regression model is 0.920. This tells us that 92.0% of the variation in the exam scores can be explained by the number of hours studied.

Feb 24, 2019 · R-squared is a measure of how well a linear regression model “fits” a dataset. Also commonly called the coefficient of determination, R-squared is the proportion of the variance in the response variable that can be explained by the predictor variable. The value for R-squared can range from 0 to 1.

Apr 22, 2022 · The coefficient of determination is often written as R 2, which is pronounced as “r squared.” For simple linear regressions, a lowercase r is usually used instead (r 2).

Sep 25, 2024 · R-squared, also known as the coefficient of determination, is a statistical measure that represents the proportion of the variance for a dependent variable that’s explained by one or more independent variables in a regression model. In simpler terms, it shows how well the data fit a regression line or curve.

R-squared is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination, or the coefficient of multiple determination for multiple regression.

Learn about the R squared of a linear regression and its properties. Discover how it is defined, calculated and interpreted.

Oct 23, 2020 · The coefficient of determination (commonly denoted R 2) is the proportion of the variance in the response variable that can be explained by the explanatory variables in a regression model. This tutorial provides an example of how to find and interpret R 2 in a regression model in R.

Coefficient of determination (R-squared) indicates the proportionate amount of variation in the response variable y explained by the independent variables X in the linear regression model. The larger the R-squared is, the more variability is explained by the linear regression model.