The answer to "what is a good R-squared value?" depends on the context and the field of study. In general, a higher R-squared value indicates that the predictor variables are more precisely able to predict the value of the response variable. However, a high or low R-squared value isnt necessarily good or bad, and it doesnt convey the reliability of the model or whether youve chosen the right regression.

In some fields, such as social sciences, even a relatively low R-squared value, such as 0.5, could be considered relatively strong. 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.

Its important to note that a low R-squared isnt always a problem, and a high R-squared doesnt automatically indicate that you have a good model. The correct R-squared value depends on the study area, the amount of variability that is inherently unexplainable, and the decision-making context. Therefore, its recommended to compare your R-squared value to those from similar studies and to consider other measures of model fit, such as the likelihood ratio.