Proceedings of Mathsport international 2017 conference

Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017. MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet. Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports

A new statistical procedure to support industrial research into new product development

In recent years, the competitive context in which goods-producing industrial companies operate has been characterized by strong dynamism and growing attention to strategies capable of maximizing customer satisfaction. In order to reach this objective, the importance of paying particular attention to the initial stages of development of a new product is widely recognized and successful companies therefore invest great resources and competences in industrial research. In this paper we propose a new statistical procedure to support the development of new, successful industrial products on the basis of their experimental performances. New product performances are modelled in a stratified one-way Analysis of Variance (ANOVA) framework and a set of non-parametric permutation tests are applied to detect significant differences in performances from which we can obtain partial rankings and a final global ranking suitable for identifying the best product

A new statistical procedure to support industrial research into new product development

none3In recent years, the competitive context in which goods-producing industrial companies operate has been characterized by strong dynamism and growing attention to strategies capable of maximizing customer satisfaction. In order to reach this objective, the importance of paying particular attention to the initial stages of development of a new product is widely recognized and successful companies therefore invest great resources and competences in industrial research. In this paper we propose a new statistical procedure to support the development of new, successful industrial products on the basis of their experimental performances. New product performances are modelled in a stratified one-way Analysis of Variance (ANOVA) framework and a set of non-parametric permutation tests are applied to detect significant differences in performances from which we can obtain partial rankings and a final global ranking suitable for identifying the best product.mixedBonnini S.; Corain L.; Salmaso L.Bonnini, Stefano; Corain, L.; Salmaso, L

Valutazione delle preferenze e customer satisfaction: un approccio basato sulla conjoint analysis e sui modelli mistura

Preference evaluation methods like Conjoint Analysis and Choice Based Conjoint Analysis have been described as the most used methods among marketing operators (Green and Srinivasan, 1990; Green et al., 2001). The concept of preference evaluation is linked to the Customer Satisfaction measurement with the latter a direct measure of preferences and expectations (Grigoroudis and Siskos, 2002). Within the choice of a suitable statistical model, Combination Uniform Binomial (CUB) models have been developed with the aim to explain the psychological mechanism underlying the choice process (D’Elia, 2003; D’Elia and Piccolo, 2005). Several model extensions have been developed (Iannario, 2013) in order to take into account the multifaceted individual choice behaviour. Within the framework of preference evaluation and Customer Satisfaction measurement, CUB models are suited to many real cases (Piccolo and D’Elia, 2008; Corduas et al., 2009; Cicia et al., 2010; Iannario et al., 2012; Iannario and Piccolo, 2012; Bordignon and Salmaso, 2013; Arboretti and Bordignon, 2014), confirming CUB models as useful and theorem based (Iannario and Piccolo, 2014) statistical models. Feeling and Uncertainty are supposed to be latent variables involved in the choice process of an item. The interpretation is very flexible with the “feeling” parameter explaining for the meaning (satisfaction, preference or attention) the measurement scale is supposed to measure. The CUB model extension involving the introduction of covariates to explain feeling and uncertainty latent variables is the main extension applied to an integrated approach