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In an experiment, identifying the experimental units is crucial as they are the primary subjects being tested. Here, the experimental units are the 350 students living in a university dormitory. These students are chosen because they are a distinct group that can give reliable data based on their behavior during the experiment. By focusing on this group, the university can effectively monitor changes and results, ensuring that the data collected represents the impact of different pricing schemes on their VoIP usage.
Understanding who your experimental units are helps in setting up the environment for collecting consistent and clear observations from the study.

Explanatory variables, also known as factors, are those elements manipulated by researchers to observe their effect on other variables. In this scenario, the explanatory variables are the pricing plans offered to students. The university has devised two types of pricing plans: a low flat rate and a variable rate that changes based on the time of the day, being higher during peak periods and much lower during off-peak times.
These variables serve as the main factors of the experiment, engineered to detect how pricing influences student behavior in terms of their VoIP service usage. By adjusting these explanatory variables, the research can analyze and interpret the resulting effects they have on the targeted outcomes.

Response variables are what the experiment is truly interested in measuring or observing; they are the outcome data upon which conclusions are drawn. For this university-based experiment, the primary response variables are

• the amount of VoIP service usage, which simply measures how often students use the service,
• the time of day when the service is used, helping illustrate patterns in peak and off-peak utilization,
• the congestion on the network, which indicates the stress on network resources due to variation in service usage.

By tracking these response variables, the university can better understand how different pricing models affect both user behavior and technical performance.

In the context of an experimental study, treatments explain how the explanatory variables are applied to the experimental units. For this experiment, the treatments refer to the different pricing schemes assigned to the students for using the VoIP service.
In more detail, the treatments involve two specific strategies: offering a constant low flat rate to some students, while others are charged rates that vary depending on whether they are using the service during peak or off-peak times. This varied application of pricing plans acts as a treatment, letting the university analyze which pricing model is more effective in not only promoting the use of VoIP services but also in managing network congestion efficiently.
Therefore, understanding the treatments lets us see how changes in one variable (pricing) can produce shifts in behavior and performance outcomes.