Co-efficient of Determination Tutorial
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Coefficient of Determination Definition
The coefficient of determination is a unit used in statistical analysis that assesses how well a model explains and predicts future outcomes. It indicates the level of explained variability in the data set. The coefficient of determination, also known as “R-squared,” is used as a guideline to calculate the accuracy of the model.
One way to interpret this figure is to say that the variables included in a given model explain approximately x% of the observed variation.
So, if the R2 = 0.50, then approximately half of the observed variation can be explained by the model.
Why we need a Coefficient of Determination?
The coefficient of determination is used to explain how much variability of 1 factor can cause by its relationship to another factor. It is relied on highly on trend analysis and is denoted as a value between 0 and 1.
The closer the value towards 1, the better the fit will be, or the relationship, between the two factors.
The coefficient of determination is the square of the correlation coefficient (R) that allows it to display the degree of linear correlation between the 2 variables.
This correlation is also known as the “goodness of fit.”A value of 1.0 displays a perfect fit, and it is thus a reliable model for future forecasts, indicating that the model explains all of the variations observed. A value of 0, on the other hand, will denote that, the model fails to accurately model the data at all. In economics, an R2 value above 0.60 is seen as worthwhile.
Co-efficient of Determination Example
Co-efficient of Determination Formula
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