Maximize and Minimize a 12" piece of wire into a square and circle 
A wire of length 12" can be bent into a circle, a square or cut into 2 pieces and make both a circle and a square. How much wire should be used for the circle if the total area enclosed by the figure(s) is to be:
a) a Maximum
b) a Minimum

What I've got so far is that the formula for the square is $A_s=\frac{1}{16}s^2$ and the circumfrance of the circle to be $P=12-c$ and area to be $A_c = \pi(\frac{P}{2\pi})^2$ where $c$ is the length of the wire for the circle and $s$ is the length of the wire for the square.
Now I know I need to differentiate these formulas to then find the max and min they both can be, but what am I differentiating with respect to? The missing variable in each of the formulas?
Also, once, I find the derivitives, what would my next steps be to minimizing and maximizing these?
And did I set the problem up correctly?
Thanks for any help

Every so often, one might mention the following sort of approach.
Let $x$ be the length of wire we will devote to the circle, and $y$ the length we will devote to the square. 
Let $A$ be the combined area of the circle and square. A calculation identical to the one done by the OP shows that
$$A=\frac{x^2}{4\pi}+\frac{y^2}{16}.$$
We want to find the values of $x$ that give maximum and minimum area, given that $x$ and $y$ are non-negative, and $x+y=12$.
Maximum and/or minimum values may be reached at an endpoint. So we compute $A$ when $x=0$, $y=12$, and also when $x=12$, $y=0$.
The remaining candidates for maximum/minimum are with $0<x<12$. At such a candidate $x$, we will have $\dfrac{dA}{dx}=0$. (We are doing one-variable calculus.)
It is easy to see that 
$$\frac{dA}{dx}=\frac{2x}{4\pi} +\frac{2y}{16}\frac{dy}{dx}.$$
But from $x+y=12$, we can see that $\dfrac{dy}{dx}=-1$.  Now we have two equations in the two unknowns $x$ and $y$. Solve for $x$, compute $A(x)$, and compare with the endpoint values.
The above procedure carries no advantage in this case, and may increase the probability of mechanical error.  However, when the "constraint" is non-linear, there can be real computational advantages to working with implicit functions, particularly if the constraint has symmetries. 
