What method is commonly used to analyze rhythmic patterns in biological signals?

Spectral analysis is the correct choice for analyzing rhythmic patterns in biological signals because it allows for the examination of frequency components within a signal. Biological signals, such as EEG or ECG, often exhibit periodicity and rhythmicity, which can be better understood by breaking down the signals into their constituent frequencies. Through techniques such as Fourier Transform, spectral analysis provides insight into how often certain rhythms occur within the signal, making it a powerful tool for detecting and analyzing oscillatory behavior.

In contrast, statistical modeling is more suited for understanding relationships between variables rather than focusing specifically on rhythmic patterns. Random sampling is generally used for data collection rather than analysis and does not provide insights into the rhythmic characteristics of biological signals. Linear regression is primarily used for modeling relationships between a dependent variable and one or more independent variables, and while it can offer insights into trends, it does not specifically target the analysis of rhythm or frequency in signals.