Abstract
Chapter 2 presents an overview of the most important designs implemented for factor screening, including data analysis in pre-response surface methodology (pre-RSM). This chapter mainly focuses on practical aspects considering a group of experimental factors at different levels at the same time, with a minimum number of experiments. Then it is discussed how to carry out studies in order to build an empirical model that correlates the information about factors and responses. Data analysis in pre-RSM is mostly represented by the well-known statistical method of multiway analysis of variance (ANOVA), whose basics are presented, together with the analysis of the validity of the results. In addition, two graphical strategies, the Pareto chart and the diagram of normal probability, are discussed. An example is used to illustrate the concepts. Suitable references are provided.
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© 2023 American Association of Pharmaceutical Scientists
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Chiappini, F.A., Teglia, C.M., Azcarate, S.M., Goicoechea, H.C. (2023). Fundamentals of Design of Experiments and Optimization: Designs for Factor Screening and Data Analysis in Pre-Response Surface Methodology. In: Breitkreitz, M.C., Goicoechea, H. (eds) Introduction to Quality by Design in Pharmaceutical Manufacturing and Analytical Development. AAPS Introductions in the Pharmaceutical Sciences, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-031-31505-3_2
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DOI: https://doi.org/10.1007/978-3-031-31505-3_2
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