@obi, Haha. It does certainly have a lot of its own words. I sometimes stop and reflect how many.
Still, I strongly believe that at least a good deal of math is actually quite accessible if well explained, and I dislike talking over people's heads. To that end, I'd like to try to briefly explain what is going on above. Of course, you didn't ask, and it's hard to get notation down well on a message board, so feel free to ignore. I just don't like people feeling alienated by my subject!
So first, a word on notation in a message board. I use the symbol ^ to mean "to the power of." So for example, 3^2 = 9 ("three squared equals 9"). I use * to mean times.
Now. Three concepts to explain.
First: sets. I'm not going to use this in much detail, so suffice it to say that a set is a bunch of things collected together. For example, I might say the set S is {Putin,Obi,Thucy}. It would then be true that "Obi is an element of S," or Obi Є S. (The symbol Є just means "is in" in the context of sets. That's all it means. So if N is the set of positive whole numbers, then 3 Є N).
Second: vector spaces.
Think of a plane (like, the infinite kind), and mark one point as zero. We can think of all the other points as in relation to this one. For example, if the plane is a (flattened) US and downtown Topeka is the zero, then Chicago would be "570 miles Northeast" (approximately, at some specific angle, not just "Northeast," obviously).
Now, this allows us to do a couple things. First, we can "add" two points on the plane. We can think of each point on the plane as having an arrow going to it from the zero. We call the arrows "vectors," and we tend to get a little fuzzy and not distinguish between a point and its arrow/vector. For example, we might think of Chicago as a point, or as an arrow 570 miles long going northeast from Topeka.
If we have two points A and B, the way we add them is we pick up the arrow that goes to B, and stick its tip at the point of the arrow that goes to A. The "sum" of the arrows is then the new end of B. For example, Denver might be the arrow 530 miles long pointing west (roughly). If A is Chicago and B is Denver, then A + B would be wherever you get by first going to Chicago, and then going 530 miles west. (I don't know where that is. I don't want to know).
OK, so much for adding "vectors" (just points!). We can also multiply them by numbers. This just scales their length by the number, and if the number is negative, it also makes them point in the opposite direction. So for example, if A is Chicago, then -0.5A is the arrow going southwest from Topeka for 285 miles. We can do this with any arrows.
Now, here is the key point, and it's somewhat confusing, but it's hopefully not too bad if you take a moment to take it in. It turns out that these two properties -- having a bunch of things that you can add together, or multiply by a number -- show up again and again in very useful ways in mathematics, and moreover, there are a LOT of useful things (theorems) that you can derive knowing only those abstract facts, and knowing nothing about the things that you're actually adding. So for this reason, mathematicians define an "abstract vector space" (or just "vector space") to be any set S of items such that you can add two of them and get another element of the set, or multiply one of them by a number and get another element of the set. (There are a few requirements -- for example, we require that A + B = B + A, as you'd expect, and so on. Nothing too surprising).
So, a plane is a vector space. It turns out that normal three-dimensional space is too (the arrows just have one more dimension to move in), etc., etc.
Third topic: polynomials. I'll stick to third-degree polynomials just for ease of notation on a message board.
Consider a rule, or function, that takes one number to another in a prescribed way, say by squaring it. You give me a number, and I square it. The common notation for this, as you may know, is f(x) = x^2. I can then plug in specific values -- f(3) = 9, f(4) = 16, etc.
A monomial is a function, like this example, where the rule consists of raising the input number to some power, and then maybe multiplying it by a constant. For example, if f(x) = 3x^4, then that is a monomial. I would have f(1) = 3, f(2) = 48, f(3) = 243, etc.
A polynomial is a function whose rule is just a sum of several monomials. For example, f(x) = x^2 + 2x. x^2 and 2x are each monomials, and f is their sum, so it is a polynomial. I have f(1) = 3 in this case, f(2) = 4, and so on.
A "third-degree polynomial" is a polynomial whose highest power is 3. That means nothing gets raised to a higher degree than 3. So f(x) = -2x^3 + x + 1 is a third-degree, while f(x) = x^4 - 1 is not.
Now, here's the interesting thing. Consider the set of all polynomials third-degree or less. That's going to be things of the form ax^3 + bx^2 + cx + d, where a, b, c, and d are fixed numbers that don't depend on x. (A few examples: x^3 + 55x^2 - 2; x^3 + 1; x^3 + x^2 - x - 1; x^2; and so on). If you add two of these, you get another third-degree (or less) polynomial, and if you multiply any of them by a number, yet again you get a third-degree (or less) polynomial: if f(x) = x^3 + x^2 - 1, then 3f(x) = 3x^3 + 3x^2 - 3.
All that means that S is actually a vector space.
Which brings us to tboin's problem. In the example, P2 is a vector space of polynomials (at least if I'm right!), and p(t) is a polynomial, and the notation
p(t) Є P2
just means that the polynomial p(t) (whatever that may be) is in the space.
For example, consider our space S of third-or-less degree polynomials, and let p(t) = t^3 + t^2 - 3t + 4, and q(t) = t^4 + 1. Then
p(t) Є S
is true, as p is third-degree; but
q(t) Є S
is false, as q is fourth-degree.
Well, I hope this was at all interesting or helpful. Sorry if not. I'm admittedly not the best teacher, and as I say, you didn't ask anyway. I just read (or perhaps misread!) some amount of math intimidation in your remarks, and I try to discourage this, as I say.