What is a weighted average in a non-stationary k-armed bandit problem?

In the book Reinforcement Learning: An Introduction (page 25), by Richard S. Sutton and Andrew G. Barto, there is a discussion of the k-armed bandit problem, where the expected reward from the bandits changes slightly over time (that is, the problem is non-stationary). Instead of updating the Q values by taking an average of all rewards, the book suggests using a constant step-size parameter, so as to give greater weight to more recent rewards. Thus:

$$Q_{n+1} = Q_n + \alpha (R_n - Q_n),$$

where $$\alpha$$ is a constant between 0 and 1.

The book then states that this a weighted average because the sum of the weights is equal to 1. What does this mean? Why is this true?

More specifically, if you denote the rewards by a vector $$X$$, the weighted average will be taking the dot product between $$X$$ and a vector $$W$$ such that $$0 \le W_i \le 1$$ and the sum of all $$W_i$$ is 1.
If each $$W_i = 1/n$$ it will be a weighted average (a.k.a the mean). Using the exponential decay $$W_i = \alpha^i/ (\sum W_i)$$ is also a weighted average.