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## Key Takeaways

- R-Squared measures the proportion of variation the model explains, whereas Adjusted R-Squared accounts for the number of predictors.
- Adjusted R-Squared penalizes the model for adding irrelevant predictors, while R-Squared may increase with added predictors.
- Adjusted R-Squared provides a more accurate representation of a model’s explanatory power, especially with multiple predictors.

**R Squared vs Adjusted R Squared **

R Squared is a statistical measuring tool that is used to describe the difference between dependent and independent variables, and it was created by Dalton. Adjusted R Squared is a mathematical measuring tool that is used to change the predictor of models in regression variables.

R Squared is a demographical type of measurement that shows the variable dissimilarities. This measuring method helps to show the proportional dispute of the dependent variable described by the independent variable.

In Contrast, Adjusted R Square is the statistical measurement and a new modified version of R Square. The predictors that do not appear in a regression model had taken by the Adjusted R Squared method.

**Comparison Table **

Parameters of Comparison | R Squared | Adjusted R Squared |
---|---|---|

Meaning | A statistical measurement uses to explain the dependent and independent variables. | Adjusted R Squared is a measurement that predicts the regression variables. |

Symbol | R Squared had symbolized as R^2. | It had shown as Adjusted R^2. |

Introduced | R Squared had introduced by Galton where he is the creator of correlation. | Adjusted R Squared is the new version model for the R Squared model. |

Formula | The formula of R Squared is R^2 = 1-(RSS/TSS). | Formulas depend upon the solving models in the Adjusted R Squared model. |

Difference | R Squared is a demographical measuring that use to find the coefficient by using dependent and independent variables. | Adjusted R Squared model will take additional input variable that predicts to solve the problems. |

**What is R Squared?**

R Squared is a demographical measure used to represent the contradictions between dependent and independent variables. The variances which are proportional are the dependent variable described by the independent variable.

R^2 = 1-(RSS/TSS)

Where the above terms describe as follows,

R^2 = coefficient determination

RSS = Sum of Squares of Residuals

TSS = Total Sum of Squares

The R Squared model cannot calculate mathematically where the values will take directly from graphs. The points of the R Squared model cannot be adjustable, and these are true values.

**What is Adjusted R Squared?**

Adjusted R Squared is a facsimile that had derived from R Squared. The Adjusted R Squared will alter the predictors in the models.

Adjusted R Squared model will take additional input variable that predicts to solve the problems. These values will calculate, and it gives the desired values than the R Squared model.

An individual will take the nearby values by taking from R Squared values. This measurement adjusts the points to fit the curve in the graphical method.

**Main Differences Between R Squared and Adjusted R Squared **

- R Squared method had used to take the values originally where Adjusted R Squared values had been calculated mathematically.
- Adjusted R Squared measurement requires the R Squared points for calculations.

**References**

- https://online.ucpress.edu/collabra/article-abstract/6/1/45/114458
- https://www.tandfonline.com/doi/abs/10.1080/00031305.2016.120048
- https://www.sciencedirect.com/science/article/pii/S0167715210001288

Last Updated : 19 August, 2023

Sandeep Bhandari holds a Bachelor of Engineering in Computers from Thapar University (2006). He has 20 years of experience in the technology field. He has a keen interest in various technical fields, including database systems, computer networks, and programming. You can read more about him on his bio page.

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