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Coefficient of Determination

The coefficient of determination (𝑹^2 ) of multiple regression is a goodness of fit measure

What is the Coefficient of Determination?

The coefficient of determination (𝑹 ^2 ) of multiple regression is a goodness of fit measure. The square of the correlation between Y and the anticipated value of Y is the coefficient of determination R^2 in multiple regression.


$ R^{2}\, = \, 1\, -\, \frac{RSS}{TSS} $

R2 = Coefficient of Determination
TSS = total sum of squares
RSS = sum of squares of residuals

Why is it important?

The most frequent way to analyse r-squared is to see how well the regression model matches the data. An r-squared of 60%, for example, indicates that 60% of the data fit the regression model. A greater r-squared suggests a better fit for the model in general.

In addition to measuring the goodness of fit, the coefficient of determination has several other important applications. For example, it can compare different regression models to see which fits the data best. It can also help to identify which variables are most important in predicting the response variable. Furthermore, the coefficient of determination can be used to assess the regression model’s reliability and determine whether the model is overfitting or underfitting the data.

It is important to note that while a high R-squared value indicates a good fit, it does not necessarily mean that the regression model is the best one to use for prediction. Other factors, such as:

  • The interpretability of the model
  • The simplicity of the variables.

It is a good idea to consider when selecting a regression model.

Some More Words

Overall, the coefficient of determination is useful in multiple regression analysis. It can help researchers evaluate their models’ strengths and reliability and make informed decisions about which variables to include in their analyses.

One important thing to remember when interpreting the coefficient of determination is that outliers can influence it in the data. Outliers are data points far away from the rest of the data and can have a disproportionate impact on the regression model. It is possible for the R-squared value when there are outliers present in the data, which can lead to inaccurate conclusions about the model’s ability to fit the data.

Another consideration when using the coefficient of determination is the possibility of multicollinearity. Multicollinearity occurs when the regression model’s two or more independent variables are highly correlated. This can make it difficult to interpret the coefficients of the variables and can lead to unstable and unreliable predictions. To address this issue, researchers can use principal component analysis or regularization methods to reduce the impact of multicollinearity on the regression model.


In conclusion, it is important to remember that the coefficient of determination is just one of many useful metrics to determine how well a regression model is doing. Other metrics include mean squared error, mean absolute error, and root means squared error. This can provide additional insights into the accuracy and precision of the model’s predictions. In the end, the choice of measure can simply determine by the type of data and the particular research questions.

Owais Siddiqui
2 min read

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