# Definitional and Computational Sum of Squares are equal

Sunday September 27, 2020

The definitional (or conceptual) and computational forms of the sum of squared deviations from the mean are equivalent, as in Equation 1.

\[ SS = \sum \left( x - \overline{x} \right)^2 = \sum x^2 - \frac{ \left( \sum x \right)^2 }{ N } \tag{1} \]

We have \( N \) numbers \( x_i \). Their mean is \( \overline{x} \).

\[ \overline{x} = \frac{ \sum_{i=1}^{N} x_i }{ N } \tag{2} \]

The limits of every sum here are the same, and always range over the numbers \( x_i \), so the limits and subscript \( i \) are not shown outside of Equation 2.

The sum of squared deviations from the mean is defined as in Equation 1.

\[ \sum \left( x - \overline{x} \right)^2 \tag{3} \]

Distributing and rearranging, Expression 3 is the same as Expression 4.

\[ \sum \left( x^2 + \overline{x} \left( \overline{x} - 2x \right) \right) \tag{4} \]

Considering the properties of sums, Expression 4 can be re-written as Expression 5.

\[ \sum x^2 + \overline{x} \left( \sum \overline{x} -2 \sum x \right) \tag{5} \]

The mean \( \overline{x} \) is a constant, so summing over it gives \( N \overline{x} \). As can be seen from Equation 2, the sum over \( x \) is also \( N \overline{x} \). Using these, Expression 5 becomes Expression 6.

\[ \sum x^2 - N \overline{x}^2 \tag{6} \]

Replacing the mean \( \overline{x} \) with its definition as in Equation 2, we arrive at the computational form.

\[ \sum x^2 - \frac{ \left( \sum x \right)^2 }{ N } \tag{7} \]

Expression 3 and Expression 7 are therefore equal, as shown in Equation 1. \( \blacksquare \)

I thought about writing this up years ago; here's an old draft. I referred to McDowell's derivation (nominally for variance) in writing the above. The computational form is nice especially if you're calculating by hand, and is (has been?) sometimes taught as a procedure, sort of like long division. I don't think teaching that is really a good idea, if what you care about is teaching statistics, but it could be a neat example in the right kind of course on implementation of algorithms (applied math or CS, maybe?).