Typically, when presented with a piece of code, your first step is to come up with a formula which gives the running time $T$ of your code as a function of the input size. In your example, the function takes an array, so a natural way to represent the input size is the number of elements in the array,
values.length, which we can call $n$.
So, we now want an expression for $T(n)$. To get this, we should assign a "cost" to each statement, and then determine how many times each statement is executed. The costs are somewhat arbitrary and may vary across real computer hardware, but some assumptions can reasonably be made; in particular, we usually assume simple statements have a constant cost with respect to the input size. In your example, we might assign costs like this:
int summation(int values)
int sum = 0; // cost = A, executed once
for(int i = 0; // cost = B, executed once
i < values.length); // cost = C, executed n + 1 times
i++) // cost = D, executed n times
sum += values[i]; // cost = E, executed n times
Now, to get the expression for $T(n)$, multiply each statement's cost by the number of times it's executed. Here, we get $T(n) = A \times 1 + B \times 1 + C \times (n + 1) + D \times n + E \times n$. This comes out to $T(n) = (A + B + C) + (C + D + E) \times n = c_0 + c_1n$ for constants $c_0, c_1$.
In this example, happily, we have a closed-form expression for $T(n)$. As such, we can make an educated guess that this function is $\Theta(n)$, since $T(n)$ is a linear function. To prove this is true, we can try to prove it's both $\Omega(n)$ and $O(n)$.
By definition, $T(n) = \Omega(n) \iff \exists c > 0, n_0 \ge 0 \mid n \ge n_0 \rightarrow T(n) \ge cn$. Our job now is to demonstrate that there is an $n_0$ and $c$ such that $T(n) \ge cn$ for all $n \ge n_0$. We have $c_0 + c_1n \ge cn$ for fixed constants $c_0, c_1$. We can solve this for $c$: $c \le c_0/n + c_1$. As long as we choose $c$ less than $c_0/n + c_1$, we have a valid choice for $c$; in particular, if we choose $c = c_1$, we satisfy the inequality for all natural numbers, and can choose $n_0 = 0$.
You can make a very similar argument for $O$; try choosing $c = c_0 + c_1$ in this case and complete the argument yourself. Once you know it's both $\Omega(n)$ and $O(n)$, you can conclude that it's $\Theta(n)$.
Of course, you don't really need to find the constants every time, but it can make an argument pretty convincing. If your $T(n)$ comes out to a expression which is the sum of powers of $n$, then you will always have that $T(n) = \Theta(n^x)$, where $x$ is the highest power to which $n$ is raised.
This gets significantly harder when $T(n)$ is recursive, i.e., when the expression for $T(n)$ involves $T$ itself; to solve problems like that, you'll often need to avail yourself of the Master Theorem or inductive proofs on the input size. Cross that bridge when you come to it, though.