Regression analysis is a statistical method used to study the relationship between a dependent variable and one or more independent variables.
One of the most commonly used methods for linear regression analysis is R-Squared.
In this article, you'll get to know what R-Squared is and the meaning of its value(s). You'll also see some of the fields where it is used.
What is R Squared?
R-Squared (R²) is a statistical measure used to determine the proportion of variance in a dependent variable that can be predicted or explained by an independent variable.
In other words, R-Squared shows how well a regression model (independent variable) predicts the outcome of observed data (dependent variable).
R-Squared is also commonly known as the coefficient of determination. It is a goodness of fit model for linear regression analysis.
What Does an R Squared Value Mean?
An R-Squared value shows how well the model predicts the outcome of the dependent variable. R-Squared values range from 0 to 1.
An R-Squared value of 0 means that the model explains or predicts 0% of the relationship between the dependent and independent variables.
A value of 1 indicates that the model predicts 100% of the relationship, and a value of 0.5 indicates that the model predicts 50%, and so on.
The formula below is mostly used to find the value of R-Squared:
R² = 1 - RSS/TSS
where,
- R² = coefficient of determination
- RSS = sum of squares of residuals
- TSS = total sum of squares
Where Is R Squared Used?
R-Squared is used by different fields. It can be used for the following:
- Risk analysis in finance.
- Marketing campaigns.
- Scientific research.
- Economics.
- Sports analysis.
Summary
In this article, we talked about R-Squared. It is a statistical method mostly used in predicting the outcome of data.
We started by looking at what R-Squared means. We then talked about the meaning of its value and how to calculate it.
Lastly, we talked about the different fields where R-Sqaured can be used.
Thank you for reading!
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