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Disegno split-plot e superfici di risposta con effetti casuali

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Abstract

Questo lavoro illustra il disegno sperimentale split-plot ponendo l’attenzione allo sviluppo che questo piano sperimentale ha ottenuto negli ultimi anni in ambito tecnologico e ambientale. In particolare, tale disegno sperimentale è qui brevemente introdotto considerando la struttura “classica” del disegno split-plot e dell’analisi della varianza ad esso corrispondente, successivamente si illustra la nuova interpretazione di questo disegno sperimentale entro la metodologia delle superfici di risposta, con particolare attenzione alle superfici di risposta con effetti casuali. Il lavoro è ampiamente integrato da esempi che possono aiutare nell’interpretazione dei concetti teorici illustrati; si presuppone comunque la conoscenza da parte del lettore dei fondamenti del disegno degli esperimenti, dell’analisi della varianza e della metodologia delle superfici di risposta, che qui è riassunta in un breve paragrafo. La struttura del lavoro è la seguente: nel primo paragrafo si introducono i primi elementi definitori del disegno split-plot; successivamente, tramite una breve rassegna, si illustra l’evoluzione metodologica che questo disegno sperimentale ha avuto negli ultimi anni. Il terzo paragrafo è dedicato all’illustrazione teorica dello split-plot, con particolare riferimento alla distinzione tra fattori whole-plot e sub-plot. Il paragrafo 1.6 illustra in breve la metodologia della superfici di risposta, ponendo l’attenzione alle superfici di risposta con effetti casuali ( mixed response surface models ) e alla definizione del modello statistico per il disegno sperimentale split-plot. L’ultimo paragrafo illustra il disegno split-plot con due esempi relativi al settore industriale-tecnologico e al settore ambientale.

Suggested Citation

  • Rossella Berni, 2014. "Disegno split-plot e superfici di risposta con effetti casuali," Econometrics Working Papers Archive 2014_10, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
  • Handle: RePEc:fir:econom:wp2014_10
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    References listed on IDEAS

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    1. Roustant, Olivier & Ginsbourger, David & Deville, Yves, 2012. "DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i01).
    2. Rossella Berni & Valeria Leonarda Scarano & Francesco Bertocci & Marcantonio Catelani, 2013. "Mixed response surface models and Bayesian analysis of variance components for electrically conductive adhesives," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 29(4), pages 387-398, July.
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    JEL classification:

    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

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