Snoring sounds is commonly known to be associated with obstructive sleep apnea (OSA). There are many studies trying to distinguish between the snoring sounds of non-OSA and those of OSA patients. However, OSA is only one of the conditions that affect the structure of upper airway. In this study, we investigated the effect of anthropometric parameters on the snoring sounds. Since snoring sounds are non-Gaussian signals by nature, we derived its Higher Order Statistical (HOS) features and investigated the statistical significance of the anthropometric parameters on each of these features. Data were collected from 40 patients with different levels of OSA. Tracheal respiratory sounds collected by a microphone placed over suprasternal notch, were recorded simultaneously with full-night Polysomnography (PSG) data during sleep. The snoring segments were identified semi-automatically from respiratory sounds using an unsupervised snore detection algorithm. The bispectrum of each SS segment was estimated. We calculated two common HOS measures, Skewness and Kurtosis, plus a new feature called Projected Median Bifrequency (PMBF) from the SS segments. Then, we investigated the statistical relationship between these features and anthropometric parameters such as height, Body Mass Index (BMI), age, gender, and Apnea-Hypopnea Index (AHI). The result showed that gender, BMI, height, and AHI are the parameters that do change the characteristics of snoring sounds significantly.