## 2 Materials and Methods
\begin{table}
\begin{tabular}{c c} \hline \hline
**Guidelines** & **Some Explanatory Notes** \\ \hline
1. Identification and definition of the problem & This is often the most important part in the success of a given experiment, often being associated with Pareto’s principle. It is key to define what the experiment is useful for, e.g., factor screening, optimization, validation, or reaffirmation of something suspected. \\ \hline
2. Identification and definition of the response variable & Selecting a contradictory response variable will always lead to inappropriate results. The experiment must also decide how to measure its value. \\ \hline
3. Identification and selection of factors and their levels & The number of factors and their levels have a connection with the logics and precision of the experiment. They are usually fixed or random, or mixed in some cases. \\ \hline
4. Selection of the type of experimental design & Curvature must be kept in mind when selecting an experimental design. The same selection is closely related to step 1. The experimenter also needs to define how many replicates are needed or can be executed, as well as the order of combinations or treatments and the order of the model. \\ \hline
5. Execution of the experiment & This implies selecting and properly setting up the experiment equipment, making sure the levels are properly set and even executing trial tests before the real experimentation begins. \\ \hline
6. Selection of the measurement device or system and definition of a proper measurement plan & This often depends on the final or technical resources available. The lower the mean percentage error of the equipment, the better. The equipment should be able to execute the measurements of all defined variables. \\ \hline
7. Selection of proper statistical software for the analysis of the collected data & This often depends on the final or technical resources available. The experimenter is advised to use professional software such as Minitab, IBM SPSS, and Statsraphics. \\ \hline
8. Interpretation of the results from the statistical analysis & This is one of the most important parts. Interpretations should be established strictly following the statistical theory; however, this should be carried out in the context of the given instance under study. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: General guidelines for designing an experiment. Even though all of the previous steps in the guidelines are important and constitute a whole, it is precisely the selection of the experimental design which is considered to be a milestone in any experimental design, given that it may make the experiment either stand out or worthless. For these purposes, it is also of particular importance to consider the time available for the realization of all experimental runs. This time has a direct relation to the number of experimental variables, the number of their levels, and the amount of data required. In a factorial design, the runs are usually represented by the value of \(n\), which refers to the number of combinations and can be easily determined using the expression \(n=k\), where \(l\) stands for the number of levels of the factors, and \(k\) refers to the number of experimental variables investigated (factors). For example, if conducting an experiment with three factors at two levels each, such as the one presented by the authors of this paper, the total number of runs or combinations would be \(2^{3}\), which yields a total of 8. At the same time, this value of \(n\) must always be multiplied by the number of replicates of the experiment. These replicates are known to be extra experimental runs with the same factor settings (levels), subject to the same sources of variability and/or conditions independently of each other.