# Why is SSE smaller for a "full" multi-regression model than for a "reduced" multi-regression model?

We're learning about multi regression in the current module of my statistics course, and the instructor noted that the sum of square errors (SSE) of a full model such as the one below:

$Y_i=\beta_0+\beta_1x_{1i}+\beta_2x_{2i}+\beta_3x_{3i}+\epsilon_i$

is going to be smaller than the SSE for any reduced model, such as the one below (which we obtain under the assumption that $\beta_1=0$):

$Y_i=\beta_0+\beta_2x_{2i}+\beta_3x_{3i}+\epsilon_i$

I'm having trouble understanding why this is true. If SSE is defined as:

$\sum^{n}_{i=1}(y_i-\hat{y_i})$

Shouldn't the full model's SSE be bigger because it has more terms?

• I think that by "multi regression", you mean "multiple regression" (which is different from "multivariate regression", so it's worth writing the extra syllable). Apr 2, 2018 at 18:58
• Note that the number of terms doesn't show up in your $\sum_{i=1}^n(y_i-\hat{y}_i)$. The only issue is how close you can get $\hat{y}_i$ to $y_i$. Apr 2, 2018 at 19:33

If $\beta_1$ is exactly zero, the SSE for full and reduced models will be identical. To the extent $\beta_1$ is not exactly zero, the component of the variance (sums of squares) of Y attributable to $x_1$ is added to the SSE, all else being equal, as it is no longer represented in the model anywhere but e (the residual variance).