This segment tackles a more complex scenario where the loop's termination condition depends on the cumulative sum within the loop itself. The speaker systematically analyzes the loop's behavior, showing how to determine the number of iterations by relating the sum to the loop's termination condition. This approach is valuable for understanding time complexity in less straightforward scenarios. This segment meticulously traces the execution of a nested loop, demonstrating a step-by-step approach to determine the number of times the inner loop's statement executes for different iterations of the outer loop. By observing the pattern, the viewer can understand how to derive the time complexity of such nested loops, which is crucial for algorithm analysis. This explains how to analyze time complexity of code snippets. Simple loops are O(n). Nested loops are typically O(n²). A less obvious nested loop example, analyzed by tracing, also proves to be O(n²). A final loop, where a variable is incremented by successively larger amounts, is shown to be O(√n).