Explaining the Relationship Between the Predictor(s) and the Response Variable Are you interested in explaining the relationship between the predictor(s) and the response variable?Ģ. Are you interested in predicting the response variable?ĭepending on the objective, the answer to “What is a good value for R-squared?” will be different. The answer to this question depends on your objective for the regression model. This leads to an important question: is this a “good” value for R-squared? This indicates that 20% of the variance in the number of flower shops can be explained by the population size. In the output of the regression results, you see that R 2 = 0.2. You fit a simple linear regression model to the dataset, using population size as the predictor variable and flower shops as the response variable. Instead, you’ll likely encounter some value between 0 and 1.įor example, suppose you have a dataset that contains the population size and number of flower shops in 30 different cities. In practice, you will likely never see a value of 0 or 1 for R-squared. A value of 1 indicates that the response variable can be perfectly explained without error by the predictor variable. A value of 0 indicates that the response variable cannot be explained by the predictor variable at all.
The value for R-squared can range from 0 to 1. 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. R-squared is a measure of how well a linear regression model “fits” a dataset.
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