When minibatch stochastic gradient descent is applied to the Airfoil Self-Noise dataset with a batch size of $100$ and a learning rate of $0.4$, it requires approximately $0.025$ seconds per epoch—comparable to full-batch gradient descent—while converging substantially faster in terms of loss reduction. Reducing the batch size to $10$ (with learning rate $0.05$) increases the epoch time to about $0.090$ seconds because smaller batches are less efficient to execute on hardware, yet this is still faster than pure SGD. A final time-versus-loss comparison plotted on a logarithmic time axis across all four methods (full-batch GD, SGD, batch size $100$, and batch size $10$) confirms that although SGD converges faster than GD in terms of examples processed, it uses more wall-clock time to reach the same loss because per-example gradient computation is not as efficient. Minibatch SGD achieves the best overall trade-off: a batch size of $100$ can even outperform full-batch GD in total runtime by making $15$ parameter updates per epoch instead of just one, while retaining the computational benefits of vectorized operations.