Weitere Kapitel dieses Buchs durch Wischen aufrufen
The data set used in Chapters 6-9 to illustrate the model building process was based on observational data: the samples were selected from a predefined population and the predictors and response were observed. The case study in the chapter is used to explain the model building process for data that emanate from a designed experiment. In a designed experiment, the predictors and their desired values are prespecified. The specific combinations of the predictor values are also prespecified, which determine the samples that will be collected for the data set. The experiment is then conducted and the response is observed. In the context model building for a designed experiment we present a strategy (Section 10.1), recommendations for evaluating model performance (Section 10.2), an approach for identifying predictor combinations that produce an optimal response (Section 10.3), and syntax for building and evaluating models for this illustration (Section 10.4).
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Box G, Hunter W, Hunter J (1978). Statistics for Experimenters. Wiley, New York. MATH
Cohn D, Atlas L, Ladner R (1994). “Improving Generalization with Active Learning.” Machine Learning, 15(2), 201–221.
Costa N, Lourenco J, Pereira Z (2011). “Desirability Function Approach: A Review and Performance Evaluation in Adverse Conditions.” Chemometrics and Intelligent Lab Systems, 107(2), 234–244. CrossRef
Cruz-Monteagudo M, Borges F, Cordeiro MND (2011). “Jointly Handling Potency and Toxicity of Antimicrobial Peptidomimetics by Simple Rules from Desirability Theory and Chemoinformatics.” Journal of Chemical Information and Modeling, 51(12), 3060–3077. CrossRef
Derringer G, Suich R (1980). “Simultaneous Optimization of Several Response Variables.” Journal of Quality Technology, 12(4), 214–219.
Mandal A, Johnson K, Wu C, Bornemeier D (2007). “Identifying Promising Compounds in Drug Discovery: Genetic Algorithms and Some New Statistical Techniques.” Journal of Chemical Information and Modeling, 47(3), 981–988. CrossRef
Myers R, Montgomery D (2009). Response Surface Methodology: Process and Product Optimization Using Designed Experiments. Wiley, New York, NY. MATH
Wager TT, Hou X, Verhoest PR, Villalobos A (2010). “Moving Beyond Rules: The Development of a Central Nervous System Multiparameter Optimization (CNS MPO) Approach To Enable Alignment of Druglike Properties.” ACS Chemical Neuroscience, 1(6), 435–449. CrossRef
Yeh I (1998). “Modeling of Strength of High-Performance Concrete Using Artificial Neural Networks.” Cement and Concrete research, 28(12), 1797–1808. CrossRef
Yeh I (2006). “Analysis of Strength of Concrete Using Design of Experiments and Neural Networks.” Journal of Materials in Civil Engineering, 18, 597–604. CrossRef
- Case Study: Compressive Strength of Concrete Mixtures
- Springer New York
- Chapter 10
Neuer Inhalt/© ITandMEDIA, Best Practices für die Mitarbeiter-Partizipation in der Produktentwicklung/© astrosystem | stock.adobe.com