Designers of embedded real-time systems derive, in general, their time parameters such as activation periods from those of sensors or actuators. By designers, we mean the team in charge of conceiving embedded real-time systems. This team includes Control Theory designers and Computer Science designers. Within this paper we present the point of view of Computer Science designers, while the periods proposed by Control Theory designers are supposed robust with respect to the physical behavior of the system. The execution times are, then, estimated by studying statically the programs structure or dynamically the programs execution. In some cases, both activation periods and execution times depend on a sensor information. For instance, they depend on the angular speed of wheels within an automotive embedded real-time system and such systems follow a rate-dependent model. Elastic tasks is another model, where one may consider execution time variation depending on the selected period. Within this paper, we are interested in describing statistically the relationship between activation periods and execution times of programs. More precisely, we study the impact of the period variation on the distributions of the execution times. To illustrate our preliminary results, we consider, as case study, the set of programs executing the autopilot of an open-source PX4 drone.