# What is the difference between the R-Squared and adjusted R-Squared when running a regression analysis?

Adjusted R-squared applies only to multiple regression

As you add more independent variables to a multiple regression, the value of R-squared increases giving you the impression that you have a better model which isn't necessarily the case. Without going in depth, the adjusted R-squared will take into account this bias of increasing R-squared.

If you examine any multiple regression results, you will note that the adjusted R-squared is ALWAYS less than R-squared because the bias has been removed.

The goal of the statistician is to optimize the best combination of independent variables such that the value of adjusted R-squared is maximized.

hope that helps

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The main difference between R-squared and adjusted R-squared lies in their ability to account for the number of predictors in a regression model. R-squared represents the proportion of the variance in the dependent variable that is explained by the independent variables. Adjusted R-squared, on the other hand, penalizes for the inclusion of unnecessary predictors in the model, providing a more accurate measure of the model's goodness of fit. Adjusted R-squared increases only if the new variable improves the model more than would be expected by chance.

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When evaluating a one-sided limit, you need to be careful when a quantity is approaching zero since its sign is different depending on which way it is approaching zero from. Let us look at some examples.

When evaluating a one-sided limit, you need to be careful when a quantity is approaching zero since its sign is different depending on which way it is approaching zero from. Let us look at some examples.

When evaluating a one-sided limit, you need to be careful when a quantity is approaching zero since its sign is different depending on which way it is approaching zero from. Let us look at some examples.

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