3 linked to the article and explained what adj. r^2 is edited Jan 9 '12 at 18:55 whuber♦ 211k3434 gold badges464464 silver badges846846 bronze badges One situation you would want to avoid $$R^2$$ is multiple regression, where adding irrelevant predictor variables to the model can in some cases increase $$R^2$$. This can be addressed by using the adjusted $$R^2$$ value instead, calculated as $$\bar{R}^2 = 1 - (1-R^2)\frac{n-1}{n-p-1}$$ where $$n$$ is the number of data samples, and $$p$$ is the number of predictors (i.e., dimension of $$x_i$$ for any data point $$i$$)regressors not counting the constant term. One situation you would want to avoid $$R^2$$ is multiple regression, where adding irrelevant predictor variables to the model can in some cases increase $$R^2$$. This can be addressed by using the adjusted $$R^2$$ value instead, calculated as $$\bar{R}^2 = 1 - (1-R^2)\frac{n-1}{n-p-1}$$ where $$n$$ is the number of data samples, and $$p$$ is the number of predictors (i.e., dimension of $$x_i$$ for any data point $$i$$). One situation you would want to avoid $$R^2$$ is multiple regression, where adding irrelevant predictor variables to the model can in some cases increase $$R^2$$. This can be addressed by using the adjusted $$R^2$$ value instead, calculated as $$\bar{R}^2 = 1 - (1-R^2)\frac{n-1}{n-p-1}$$ where $$n$$ is the number of data samples, and $$p$$ is the number of regressors not counting the constant term. 2 linked to the article and explained what adj. r^2 is edit approved Jan 9 '12 at 18:55 highBandWidth 1,23022 gold badges1414 silver badges3030 bronze badges One situation you would want to avoid R^2$$R^2$$ is multiple regression, where adding irrelevant predictor variables to the model can in some cases increase R^2$$R^2$$. This can be addressed by using the adjusted R^2adjusted $$R^2$$ value instead, calculated as $$\bar{R}^2 = 1 - (1-R^2)\frac{n-1}{n-p-1}$$ where $$n$$ is the number of data samples, and $$p$$ is the number of predictors (i.e., dimension of $$x_i$$ for any data point $$i$$). One situation you would want to avoid R^2 is multiple regression, where adding irrelevant predictor variables to the model can in some cases increase R^2. This can be addressed by using the adjusted R^2 value instead. One situation you would want to avoid $$R^2$$ is multiple regression, where adding irrelevant predictor variables to the model can in some cases increase $$R^2$$. This can be addressed by using the adjusted $$R^2$$ value instead, calculated as $$\bar{R}^2 = 1 - (1-R^2)\frac{n-1}{n-p-1}$$ where $$n$$ is the number of data samples, and $$p$$ is the number of predictors (i.e., dimension of $$x_i$$ for any data point $$i$$). 1 answered Jul 20 '11 at 20:43 jedfrancis 34111 silver badge55 bronze badges One situation you would want to avoid R^2 is multiple regression, where adding irrelevant predictor variables to the model can in some cases increase R^2. This can be addressed by using the adjusted R^2 value instead.