Modulation classification plays a crucial role in wireless communication systems, enabling applications such as cognitive radio, spectrum monitoring, and electronic warfare. Conventional techniques often involve deep learning or complex feature extraction, which, while effective, require substantial computational resources and memory. An early approach by Chan and Gadbois in 1985 introduced a theoretical method for modulation classification using a mathematically derived parameter called R. The authors proved that the R value - the ratio of the variance to the square of the mean of the signal envelope - can be a distinguishing feature for classification. In this work, we revisit the R value and show that classification accuracy can be improved further through statistical methods. We extend R-value analysis to demonstrate its effectiveness even after signals are transformed using the Hilbert transform followed by the Short-Time Fourier Transform (STFT). Our analysis includes testing on 300000 signals across AM, DSB, and SSB classes, with each class having 100000 random variations. On average, we achieve 98.60, 97.30, and 97.90 percent classification accuracy for AM, DSB, and SSB signals after applying the Hilbert transform. Similar or better accuracies are observed after applying the STFT, reaching 98.80, 99.10, and 99.00 percent, respectively, for AM, DSB, and SSB types.