Rules of Double Sums What are the (most important) rules of double sums? Below are some rules I encountered - are they all correct and complete? Offerings of clear intuition or proofs (or other additions) are most welcome to remove confusion.

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*General case: $$\sum_{i=1}^m[x_i] \cdot \sum_{j=1}^n[y_j] = \sum_{i=1}^m\sum_{j=1}^n[x_iy_j]$$

*Less general case ($m=n$): $$\sum_{i=1}^n[x_i] \cdot \sum_{j=1}^n[y_j] = \sum_{i=1}^n\sum_{j=1}^n[x_iy_j] = \sum_{i=1}^n[x_iy_i] + \sum_{i=1}^n\sum_{j=1 \\ j \neq i}^n[x_iy_j]$$

*Special case ($m=n,x_i=y_i$): $$\left(\sum_{i=1}^n[x_i]\right)^2 = \sum_{i=1}^n[x_i] \cdot \sum_{j=1}^n[x_j] = \sum_{i=1}^n\sum_{j=1}^n[x_ix_j] = \sum_{i=1}^n[x_i^2] + \sum_{i=1}^n\sum_{j=1 \\ j \neq i}^n[x_ix_j]$$
Question related to (3): why suddenly an index $j$ appears (initially, we had only $i$)?
Furthermore, in relation to (3) there is a theorem given in my textbook without intuition/proof:

When we work with double sums, the following theorem is of special interest; it is an immediate consequence of the multinominal expansion of $(x_1 + x_2 + \ldots + x_n)^2$:
Theorem: $$\sum_{i<j}\sum[x_ix_j] = \frac{1}{2}\left[\left(\sum_{i=1}^n[x_i]\right)^2 - \sum_{i=1}^n[x_i^2]\right], \text{ where } \sum_{i<j}\sum[x_ix_j] = \sum_{i=1}^{n-1}\sum_{j=i+1}^n[x_ix_j].$$

What is the special interest/purpose of this theorem (when is it useful?) and what is the relation with (3) above?

Explanation to (3): Given relation is 
\begin{align}
\left(\sum_{i = 1}^{n} x_{i} \right)^{2} & = \sum_{i = 1}^{n} x_{i} \sum_{j = 1}^{j} x_{j} \tag{1} \\
& = \sum_{i = 1}^{n} \sum_{j = 1}^{n}x_{i}x_{j} \tag{2} \\
& = \sum_{i = 1}^{n}x_{i}^{2} + \sum_{i = 1}^{n}\sum_{\substack{j = 1 \\ j \neq i}}^{n} x_{i}x_{j} \tag{3}
\end{align}. 
To understand this consider a simple (still general) example of $n = 3$. So you have 
\begin{align}
\left(\sum_{i = 1}^{n = 3} x_{i} \right)^{2} & = \left( x_{1} + x_{2} + x_{3} \right)^{2} = x_{1}^{2} + x_{2}^{2} + x_{3}^{2} + 2 x_{1}x_{2} + 2x_{2}x_{3} + 2x_{3}x_{1} \\
& = \underbrace{x_{1}^{2} + x_{2}^{2} + x_{3}^{2}}_{\text{squared terms}} + \underbrace{x_{1}x_{2} + x_{1}x_{2} + x_{2}x_{3} + x_{2}x_{3} + x_{3}x_{1} + x_{3}x_{1}}_{\text{cross-product terms}} \tag{4}
\end{align}
Now consider the following 
\begin{align}
 \sum_{i = 1}^{n = 3} x_{i} \sum_{j = 1}^{j = 3} x_{j} & = (x_{1} + x_{2} + x_{3}) (x_{1} + x_{2} + x_{3}) \\
& = x_{1}^{2} + x_{1}x_{2} + x_{1}x_{3} + x_{2}x_{1} + x_{2}^{2} + x_{2}x_{3} + x_{3}x_{1} + x_{3}x_{2} + x_{3}^{2} \\
& = \underbrace{x_{1}^{2} + x_{2}^{2} + x_{3}^{2}}_{\text{squared terms}} + \underbrace{x_{1}x_{2} + x_{1}x_{2} + x_{2}x_{3} + x_{2}x_{3} + x_{3}x_{1} + x_{3}x_{1}}_{\text{cross-product terms}} \tag{5}
\end{align}
Looking at (4) and (5) you can see that equality in (1) is true and the proof can be extended to any arbitrary number $n$.
Similarly, 
\begin{align}
& \sum_{i = 1}^{n = 3} \sum_{j = 1}^{n = 3}x_{i}x_{j} \\
= & \sum_{i = 1}^{n = 3} \left[ x_{i} x_{1} + x_{i} x_{2} + x_{i}x_{3} \right] \\
= & \left[ x_{1} x_{1} + x_{1} x_{2} + x_{1}x_{3} \right] + \left[ x_{2} x_{1} + x_{2} x_{2} + x_{2}x_{3} \right] + \left[ x_{3} x_{1} + x_{3} x_{2} + x_{3}x_{3} \right] \\
= & \underbrace{x_{1}^{2} + x_{2}^{2} + x_{3}^{2}}_{\text{squared terms}} + \underbrace{x_{1}x_{2} + x_{1}x_{2} + x_{2}x_{3} + x_{2}x_{3} + x_{3}x_{1} + x_{3}x_{1}}_{\text{croos-product terms}} \tag{6}
\end{align}
Hence (6) and (4) explains the equality in (2). Now equality in (3) is merely the separation of the squared terms and the cross product terms. 
\begin{align}
& \sum_{i = 1}^{n = 3}x_{i}^{2} + \sum_{i = 1}^{n = 3}\sum_{\substack{j = 1 \\ j \neq i}}^{n} x_{i}x_{j} \\
= & \underbrace{(x_{1}^{2} + x_{2}^{2} + x_{3}^{2})}_{\text{squared terms}} + \underbrace{\left[ \underbrace{x_{1} (x_{2} + x_{3})}_{i = 1, j \neq 1} + \underbrace{x_{2}(x_{1} + x_{3})}_{i = 2, j \neq 2} + \underbrace{x_{3}(x_{1} + x_{2})}_{{i = 3, j \neq 3}}\right]}_{\text{cross-product terms}} \tag{7}
\end{align}
Hence (7) and (4) explains the equality in (3) and this completes the explanation.
