5.5 until p.179 (conditional expectation), 7.1 until p.291 (Example 4 exclusively) due on 11/14

Difficult
The definition of conditional mass function is very similar to the definition of conditional probability for events. However, the denominator in the definition confuses me. Shouldn't the denominator be fY(y) instead of fY(x)? That way, it would be analogous to the conditional probability for events. Also, the examples use fY(y) instead. In section 7.1, I'm not sure what the book means by "state space." Is this similar to a sample space, only instead the number of possible outcomes is uncountable? I also don't fully understand the proof of P(X=x) = 0 if F(x) is continuous. I'm not sure how they got (x - 1/n). And lastly, in the definition of a continuous random variable, does the v in the integral stand for anything?
Reflective
Even though chapter 5 deals with two random variables, I like how conditional probability is relatively the same as when dealing with events. Although this is "new" material, it's more like a reminder of what we've previously learned. The same is true for the distribution function F in section 7.1. At this point, I feel a lot of the definitions are being reapplied to different situations.