http://hdl.handle.net/2117/3408 Sat, 18 May 2013 19:06:07 GMT 2013-05-18T19:06:07Z webmaster.bupc@upc.edu Universitat Politècnica de Catalunya. Servei de Biblioteques i Documentació no http://hdl.handle.net/2117/17274 Title: PRESTON: Juego competitivo online para el aprendizaje de la estadística Authors: Camps Lorente, Oriol; Rodero de Lamo, Lourdes; Sánchez Espigares, Josep Anton; Marco Almagro, Lluís; Tort-Martorell Llabrés, Xavier; Puig Oriol, Xavier; Riba Civil, Alexandre Abstract: Presentamos un caso de rediseño de las prácticas de una asignatura de estadística. Tras realizar un diagnóstico del diseño previo (teniendo en cuenta la introducción del EEES), se propuso un juego competitivo on-line de toma de decisiones basada en la estadística. Éste utiliza datos simulados y una web en la que los estudiantes (1) adquieren datos consumiendo un presupuesto, (2) introducen las decisiones que toman, y (3) hacen el seguimiento de su posición en el juego. El formato on-line permite flexibilidad y posibilidades de auto-aprendizaje por parte de los estudiantes. El resultado final (PRESTON, PRácticas de ESTadística ON-line) ha sido ya probado con grupos de estudiantes. Fri, 11 Jan 2013 10:09:04 GMT http://hdl.handle.net/2117/17274 2013-01-11T10:09:04Z Camps Lorente, Oriol; Rodero de Lamo, Lourdes; Sánchez Espigares, Josep Anton; Marco Almagro, Lluís; Tort-Martorell Llabrés, Xavier; Puig Oriol, Xavier; Riba Civil, Alexandre no Presentamos un caso de rediseño de las prácticas de una asignatura de estadística. Tras realizar un diagnóstico del diseño previo (teniendo en cuenta la introducción del EEES), se propuso un juego competitivo on-line de toma de decisiones basada en la estadística. Éste utiliza datos simulados y una web en la que los estudiantes (1) adquieren datos consumiendo un presupuesto, (2) introducen las decisiones que toman, y (3) hacen el seguimiento de su posición en el juego. El formato on-line permite flexibilidad y posibilidades de auto-aprendizaje por parte de los estudiantes. El resultado final (PRESTON, PRácticas de ESTadística ON-line) ha sido ya probado con grupos de estudiantes. http://hdl.handle.net/2117/13215 Title: Statistical methods in kansei engineering: a case of statistical engineering Authors: Marco Almagro, Lluís; Tort-Martorell Llabrés, Xavier Abstract: Kansei Engineering (KE) is a technique used to incorporate emotions in the product design process. Its basic purpose is discovering in which way some properties of a product convey certain emotions in its users. It is a quantitative method, and data is typically collected using questionnaires. Japanese researcher Mitsuo Nagamachi is the founder of Kansei Engineering. Products where KE has been successfully applied include cars, phones, packaging, house appliances, clothes or websites, among others. Kansei Engineering studies typically follow a model with three main steps: (1) spanning the semantic space: defining the responses, those emotions that will be studied; (2) spanning the space of properties: deciding on the technical properties of the products that can be freely changed and that might affect the responses (factors in a factorial design) and (3) the synthesis phase, where both spaces are linked (that is, how each factor affects each response is discovered). The procedure resembles that of an experimental design in an industrial context. However, practitioners of KE are hardly ever statisticians. Many well-known statistical methods are commonly used in KE, such as principal component analysis and regression analysis, but the techniques are sometimes misused. Furthermore, the discipline could benefit from a more extensive use of statistical methods (some of them of higher complexity, but easily implemented with existing statistical software). Statistics is thus essential in Kansei Engineering. But if statisticians do not enter into this arena, others will do, as there is a real need and interest in the topic. Kansei Engineering is a good area of application of what Roger W. Hoerl and Ron Snee call statistical engineering: focusing not in advancement of statistics – developing new techniques, fine tuning existing ones – but on how current techniques can be best used in a new area. The aim of this paper is presenting the fundamentals of Kansei Engineering while giving a practical example of statistical engineering in a promising field. Fri, 16 Sep 2011 10:58:49 GMT http://hdl.handle.net/2117/13215 2011-09-16T10:58:49Z Marco Almagro, Lluís; Tort-Martorell Llabrés, Xavier no Kansei Engineering (KE) is a technique used to incorporate emotions in the product design process. Its basic purpose is discovering in which way some properties of a product convey certain emotions in its users. It is a quantitative method, and data is typically collected using questionnaires. Japanese researcher Mitsuo Nagamachi is the founder of Kansei Engineering. Products where KE has been successfully applied include cars, phones, packaging, house appliances, clothes or websites, among others. Kansei Engineering studies typically follow a model with three main steps: (1) spanning the semantic space: defining the responses, those emotions that will be studied; (2) spanning the space of properties: deciding on the technical properties of the products that can be freely changed and that might affect the responses (factors in a factorial design) and (3) the synthesis phase, where both spaces are linked (that is, how each factor affects each response is discovered). The procedure resembles that of an experimental design in an industrial context. However, practitioners of KE are hardly ever statisticians. Many well-known statistical methods are commonly used in KE, such as principal component analysis and regression analysis, but the techniques are sometimes misused. Furthermore, the discipline could benefit from a more extensive use of statistical methods (some of them of higher complexity, but easily implemented with existing statistical software). Statistics is thus essential in Kansei Engineering. But if statisticians do not enter into this arena, others will do, as there is a real need and interest in the topic. Kansei Engineering is a good area of application of what Roger W. Hoerl and Ron Snee call statistical engineering: focusing not in advancement of statistics – developing new techniques, fine tuning existing ones – but on how current techniques can be best used in a new area. The aim of this paper is presenting the fundamentals of Kansei Engineering while giving a practical example of statistical engineering in a promising field.