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Published by MBA Skool Team, Last Updated: January 19, 2014

A statistical measure to assess how well a model explains and predicts future outcomes. Co-efficient of determination is commonly known as R-square.

Mathematical representation

The simple linear regression equation is E(Y) = a + bX Where, a is the Y intercept of the regression line and b is the slope of the regression line. E(Y) is expected value for given X. Now, for linear regression SSR, SSE and SST are defined as, SST = total sum of squares = ∑(y SSR = sum of squares due to regression= ∑ (ŷ SSE = sum of squares due to error = ∑ (y SST = SSR +SSE The coefficient of determination is r Where, SSR = sum of squares due to regression SST = total sum of squares |

The value of co-efficient of determination varies between 0 and 1. The correlation is very strong the value of co-efficient will be near to one. If the value is near to zero, the regression model isn’t good enough to describe the data set.

This article has been researched & authored by the Business Concepts Team. It has been reviewed & published by the MBA Skool Team. The content on MBA Skool has been created for educational & academic purpose only.

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