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oneloop
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Generally speaking, $R^2$ always increases as you increase the number of variables in your model, so by itself it is not a good criterion to know when you should stop adding variables. Instead you should use a different number that takes in some sense measures that "higher $R^2$ is better, but you don't want too many variables either". Quantities that measure this are the adjusted $R^2$, the AIC, and the BIC.

In your specific situation, since you have two variables with moderately high correlation, I would also try some sort of dimensionality reduction algorithm, and they try a linear regression on just two variables. I would look into PCA for its simplicity.

Generally speaking, $R^2$ always increases as you increase the number of variables in your model, so by itself it is not a good criterion to know when you should stop adding variables. Instead you should use a different number that takes in some sense measures that "higher $R^2$ is better, but you don't want too many variables either". Quantities that measure this are the adjusted $R^2$, the AIC, and the BIC.

In your specific situation, since you have two variables with moderately high correlation, I would also try some sort of dimensionality reduction algorithm, and they try a linear regression on just two variables. I would look into PCA for its simplicity.

Generally speaking, $R^2$ always increases as you increase the number of variables in your model, so by itself it is not a good criterion to know when you should stop adding variables. Instead you should use a different number that in some sense measures that "higher $R^2$ is better, but you don't want too many variables either". Quantities that measure this are the adjusted $R^2$, the AIC, and the BIC.

In your specific situation, since you have two variables with moderately high correlation, I would also try some sort of dimensionality reduction algorithm, and they try a linear regression on just two variables. I would look into PCA for its simplicity.

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oneloop
  • 648
  • 5
  • 15

Generally speaking, $R^2$ always increases as you increase the number of variables in your model, so by itself it is not a good criterion to know when you should stop adding variables. Instead you should use a different number that takes in some sense measures that "higher $R^2$ is better, but you don't want too many variables either". Quantities that measure this are the adjusted $R^2$, the AIC, and the BIC.

In your specific situation, since you have two variables with moderately high correlation, I would also try some sort of dimensionality reduction algorithm, and they try a linear regression on just two variables. I would look into PCA for its simplicity.