Big O notation describes how well an algorithm scales as the size of the input approaches a large number or infinity. The notation approximates the worst-case (upper-bound) performance of an algorithm.
c is constant and
n is the number of inputs.
2) O is order
Quadratic time every item in the list (aka n for the input size), we have to do n more operations. So n * n == n^2
Lineaer time, every item in the list, we have to do n operations.
Constant time means is no matter how big our input is, it always takes the same amount of time to compute things.