TP402 - Estatística Multivariada Aplicada aos Dados Sensoriais - 2S/2024 Imprimir

Ementa:

Métodos estatísticos multivariados aplicados aos dados obtidos em ciência sensorial e estudos de consumidor. A escolha do método estatístico multivariado adequado para analisar os dados sensoriais obtidos. Análise aplicada de dados, uso de softwares estatísticos e interpretação dos resultados.

Bibliografia:

• ESCOFIER, B. & PAGÈS, J. Analyses factorielles simples et multiples: Objectifs, Méthodes et Interprétation. 4e. ed. Dunod, Paris. 2008. 327p.
• GABRIEL K.R. The biplot graphic display of matrices with application to principal component analysis. Biometrika, 58, 453-467. 1971.
• HAIR, J.F., ANDERSON, R.E., TATHAM, R.L; TRAD. SANT´ANNA, A.S.; CHAVES NETO, A. Análise multivariada de dados. 5a. ed., Bookman Cia Editora. 2007. 593p.
• JOLLIFFE I.T. Principal component analysis, Second Edition. Springer, New York. 2002. KRYSTALLIS, A. Motivation and cognitive structures of store versus manufacturer brand consumers. JournalofConsumerBehaviour. 14 (4) p. 270-284. 2015.
• MEULLENET,J.F., XIONG, R., FINDLAY, C. Multivariate and probabilistic analyses of sensory science problems. Wiley: New York. 2007. 248p.
• MOITA NETO, J.M. & MOITA, G.C. Uma introdução à análise exploratória de dados multivariados. Química Nova, São Paulo, SP: 21 (4), 467-469, 1998.
• NAES, T.; BROCKHOFF, P.B.; TOMIC, O. Statistics for Sensory and Consumer Science. United Kingdown John Wiley& Sons Ltd. 2010. 297p.
• OLSON, J.C. Theoretical Foundations of means-end chains. Werbeforschung&Praxis, 5, 174-178. 1989
• ROBERT, P. & ESCOUFIER, Y. An unifying tool for linear multivariate methods. The RV coefficient. AppliedStatistics, 25 (3), 257-265. 1976.
• SANTAROSA, M. & GUINARD, J-X. Means-end chains analysis of extra virgin olive oil purchase and consumption behavior. FoodQualityandPreference. 22 (3) 304-316. 2011.
• TENENHAUS, M., PAGÈS, J., AMBROISINE L. AND & GUINOT, C. PLS methodology for studying relationships between hedonic judgments and product characteristics. Food Quality and Preference. 16, 4, pp 315-325. 2005. TUKEY, J. W. The future of data analysis. The annalsofmathematicalstatistics. 33 (1): 1–67. ISSN 0003-4851. 1962.
• ESCOFIER, B. & PAGÈS, J. Analyses factorielles simples et multiples: Objectifs, Méthodes et Interprétation. 4e. ed. Dunod, Paris. 2008. 327p.
• GABRIEL K.R. The biplot graphic display of matrices with application to principal component analysis. Biometrika, 58, 453-467. 1971.
• HAIR, J.F., ANDERSON, R.E., TATHAM, R.L; TRAD. SANT´ANNA, A.S.; CHAVES NETO, A. Análise multivariada de dados. 5a. ed., Bookman Cia Editora. 2007. 593p.
• JOLLIFFE I.T. Principal component analysis, Second Edition. Springer, New York. 2002. KRYSTALLIS, A. Motivation and cognitive structures of store versus manufacturer brand consumers. JournalofConsumerBehaviour. 14 (4) p. 270-284. 2015.
• MEULLENET,J.F., XIONG, R., FINDLAY, C. Multivariate and probabilistic analyses of sensory science problems. Wiley: New York. 2007. 248p.
• MOITA NETO, J.M. & MOITA, G.C. Uma introdução à análise exploratória de dados multivariados. Química Nova, São Paulo, SP: 21 (4), 467-469, 1998.
• NAES, T.; BROCKHOFF, P.B.; TOMIC, O. Statistics for Sensory and Consumer Science. United Kingdown John Wiley& Sons Ltd. 2010. 297p.
• OLSON, J.C. Theoretical Foundations of means-end chains. Werbeforschung&Praxis, 5, 174-178. 1989
• ROBERT, P. & ESCOUFIER, Y. An unifying tool for linear multivariate methods. The RV coefficient. AppliedStatistics, 25 (3), 257-265. 1976.
• SANTAROSA, M. & GUINARD, J-X. Means-end chains analysis of extra virgin olive oil purchase and consumption behavior. FoodQualityandPreference. 22 (3) 304-316. 2011.
• TENENHAUS, M., PAGÈS, J., AMBROISINE L. AND & GUINOT, C. PLS methodology for studying relationships between hedonic judgments and product characteristics. Food Quality and Preference. 16, 4, pp 315-325. 2005. TUKEY, J. W. The future of data analysis. The annalsofmathematicalstatistics. 33 (1): 1–67. ISSN 0003-4851. 1962.
   

Ano de Catálogo: 2024

Créditos: 4