Is an R2 of 0.5 good?
Matthew Wilson
Updated on January 17, 2026
Since R2 value is adopted in various research discipline, there is no standard guideline to determine the level of predictive acceptance. Henseler (2009) proposed a rule of thumb for acceptable R2 with 0.75, 0.50, and 0.25 are described as substantial, moderate and weak respectively.
What does an R2 of 0.5 mean?
Any R2 value less than 1.0 indicates that at least some variability in the data cannot be accounted for by the model (e.g., an R2 of 0.5 indicates that 50% of the variability in the outcome data cannot be explained by the model).What is a good R2 level?
In other fields, the standards for a good R-Squared reading can be much higher, such as 0.9 or above. In finance, an R-Squared above 0.7 would generally be seen as showing a high level of correlation, whereas a measure below 0.4 would show a low correlation.What does an R2 value of 0.05 mean?
2. low R-square and high p-value (p-value > 0.05) It means that your model doesn't explain much of variation of the data and it is not significant (worst scenario)Is 0.6 A good R2 value?
Generally, an R-Squared above 0.6 makes a model worth your attention, though there are other things to consider: Any field that attempts to predict human behaviour, such as psychology, typically has R-squared values lower than 0.5. Humans are inherently difficult to predict!R-squared, Clearly Explained!!!
What is a weak R-squared value?
- if R-squared value 0.3 0.7 this value is generally considered strong effect size, Ref: Source: Moore, D. S., Notz, W.Why is my R-squared so low?
A low R-squared value indicates that your independent variable is not explaining much in the variation of your dependent variable - regardless of the variable significance, this is letting you know that the identified independent variable, even though significant, is not accounting for much of the mean of your ...Is a low R-squared good?
R-squared does not indicate whether a regression model is adequate. You can have a low R-squared value for a good model, or a high R-squared value for a model that does not fit the data! The R-squared in your output is a biased estimate of the population R-squared.How do you tell if a regression model is a good fit?
The best fit line is the one that minimises sum of squared differences between actual and estimated results. Taking average of minimum sum of squared difference is known as Mean Squared Error (MSE). Smaller the value, better the regression model.How do you interpret r2?
Interpretation of R-SquaredFor example, an r-squared of 60% reveals that 60% of the variability observed in the target variable is explained by the regression model. Generally, a higher r-squared indicates more variability is explained by the model.