The future of companies that are founded on the development of new and innovative products is threatened when competitors reverse engineer and imitate the products. If the original developers could predict how long it would take a competitor to reverse engineer a product, it may be possible for them to delay, if not prevent, that competitor's entry into the market. Metrics and measures have been developed that can estimate the time it would take an individual to reverse engineer a product. The main purpose of these metrics and measures is to help designers determine how quickly a competitor could reverse engineer a product and develop and market a competing product. A critical parameter of these metrics is the flow rate of information (how quickly information can be extracted from a product), which is a parameter unique to each individual. This thesis seeks to establish a method for creating probability distributions that could be used to select a reasonable flow rate for an individual, by using data collected on the initial flow rate of multiple individuals.