**Coefficient of Determination:** Now that we have values of SSR, SSE, and SST. Let’s go ahead and find the coefficient of determination represented as \(R^2\). It is a statistical technique to measure the goodness of fit.

R-squared is generally interpreted as the percentage. If SSR is large, more SST is used and thus, SSE is smaller relative to the total. This ratio acts as a percentage.

Mathematically, it’s the sum of squares regression divided by the Total Sum of Squares.

\(R^2 = \frac{Sum of Squared Regression}{Total Sum of Squares} = \frac{SSR}{SST} \)

**Note-1:** The value is always between 0(0%) and 1(100%).

**Note-2:** Larger values of \(R^2\) suggest that our linear model is a good fit for the data we provided.

In our case,

\(R^2 = \frac{119872.9743}{124337.5} = 0.9641\) or 96.41%

**Conclusion:** We can conclude that 96.41% of the total sum of squares can be explained by our regression equation to predict house prices. Thus the error percentage is less than 4% which implies a GOOD FIT for our model.

While coding in S2, we include the following line to print the coefficient of determination.