3 linked to the article and explained what adj. r^2 is
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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
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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 $i$).

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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.