Data mining and quality in service industry: review and some applications

In Service Industry the main goal of Data Mining is to improve the Quality of the interface and the interaction between the organization system and the respective costumers and needs. Now-a-days with the technological advances, a new challenge on exploring data and extrapolating patterns has emerged. Data mining, as an exploratory data analysis tool used in large and complex data sets, allow discovering new snippets of knowledge and patterns in these vast amounts of data, and this is very often crucial on information leading to a higher position, in very competitive markets. In our work we will review some applications of Data Mining in Service Industry and explore some Quality related topics

Elementos de estatística descritiva

Pretende-se com o presente texto, uma abordagem aos principais tópicos usualmente desenvolvidos em Estatística Descritiva, para apoio ao módulo introdutório sobre Estatística Descritiva inserido na unidade curricular de Matemática do Curso de Qualificação para Estudos Superiores da Universidade Aberta. O principal objetivo é fornecer e/ou relembrar um conjunto de conceitos, habitualmente adquiridos pelos estudantes ao nível do ensino secundário, que permita uma melhor adaptação às unidades curriculares da área da estatística lecionadas em licenciaturas das áreas da matemática, informática, ciências, economia e gestão

Application of BIBDR in health sciences using R

Conferência realizada em Rhodes, Grécia, de 23-28 de setembro de 2019.The role of Experimental Design is very well known, considering applications to a broad range of areas, such as Agriculture, Biology, Medicine, Industry, Education, Economy, Engineering and Food Consumption Sciences. Motivated by the variety of problems faced in the several areas and simultaneously taking advantage of the emerging technological developments, new theoretical results, as well as new designs and structures, have been developed by researchers and practitioners accordingly to the needs. Experimental Design got a place among the most important statistical methodologies and, mainly because of allowing to separate variation sources, since the last century it has been strongly recommended for Health Sciences studies. In this area, particular attention has been devoted to Randomized Complete Block Designs and to Balanced Incomplete Block Designs (BIBD) - which allow testing simultaneously a number of treatments bigger than the block size. Thus, after a brief review of some particular BIBD properties and of BIBDR - Balanced Incomplete Blocks with Block Repetition, an applications to Health Sciences simulated data is illustrated, by exploring R softwareinfo:eu-repo/semantics/publishedVersio

The impact of distributional shape on the power of randomization tests for two independent groups: a simulation study using small balanced samples

The importance of randomization tests is very well known in experimental research, particularly in biometry. The aim of the resent research is to evaluate the impact of distributional shape on the power of the randomization test for difference between the means of two independent groups (with n1=n2=16). To manipulate shape in terms of asymmetry and kurtosis, we used g-and-h distributions. We evaluated the power of the randomization test, and also the power of the Student-t test, as a comparison standard, with data simulated from 12 g-and-h distributions for seven values of effect size. For each condition, we generated 20000 samples, and for each one the power of randomization tests was estimated using 1000 permutations. We set the value of Type I error probability at 0.05. The results show gains in power for both tests with increasing skewness and/or kurtosis, with a slight advantage for the randomization tests over the Student-t test

Application of information geometry in incomplete block designs: towards statistical efficiency

IMS, CEAULinfo:eu-repo/semantics/publishedVersio

Analysis of residuals and adjustment in JRA

Joint Regression Analysis (JRA) is based in linear regression applied to yields, adjusting one linear regression per cultivar. The environmental indexes in JRA correspond to a non observable regressor which measures the productivity of the blocks in the field trials. Usually zig-zag algorithm is used in the adjustment. In this algorithm, minimizations for the regression coefficients alternate with those for the environmental indexes. The algorithm has performed very nicely but a general proof of convergence to the absolute minimum of the sum of squares of residues is still lucking. We now present a model for the residues that may be used to validate the adjustments carried out by the zig-zag algorithm

An approach to distribution of the product of two normal variables

The distribution of product of two normally distributed variables come from the first part of the XX Century. First works about this issue were [1] and [2] showed that under certain conditions the product could be considered as a normally distributed. A more recent approach is [3] that studied approximation to density function of the product using three methods: numerical integration, Monte Carlo simulation and analytical approximation to the result using the normal distribution. They showed as the inverse variation coefficient µ/σ increases, the distribution of the product of two independent normal variables tends towards a normal distribution. Our study is focused in Ware and Lad approaches. The objective was studying which factors have more influence in the presence of normality for the product of two independent normal variables. We have considered two factors: the inverse of the variation coefficient value µ/σ and the combined ratio (product of the two means divided by standard deviation): (µ1µ2(/σ for two normal variables with the same variance. Our results showed that for low values of the inverse of the variation coefficient (less than 1) normal distribution is not a good approximation for the product. Another one, influence of the combined ratio value is less than influence of the inverse of coefficients of variation value.info:eu-repo/semantics/publishedVersio

Estabilidade em análise conjunta de regressões e condução dinâmica de planos de melhoramento

Tese de Doutoramento em Matemática na especialidade de Modelação Estatística apresentada à Universidade AbertaA Análise Conjunta de regressões, ACR, tem sido uma técnica bastante utilizada no domínio da avaliação de cultivares. O principal objectivo desta dissertação é a extensão da aplicação da técnica ACR, na medida em que passamos duma análise usual, em que se tem em atenção apenas as produções de cada cultivar, para o caso em que se considera o valor económico, traduzido pelo peso específico. A aplicação da técnica consistia até agora, no ajustamento de regressões lineares da produção de cada cultivar no índice ambiental. Aprofundaremos a sua aplicação a planos de melhoramento, logo a casos em que se tem um número alargado de anos de ensaios efectuados. Abordaremos a problemática associada à convergência do algoritmo zig-zag, apresentando uma validação de ajustamentos efectuada, através dum modelo linear para os resíduos. Daremos ênfase ao conceito de quase normalidade multiplicativa, através de um conjunto de simulações que nos permitirão tirar conclusões importantes tendo em vista a aplicação prática.Joint Regression Analysis, JRA, has been a widely used technique on the evaluation of cultivars. The aim of this dissertation is to extend the application of JRA, as we go from a regular analysis in which is paid attention only to the production of each cultivar, to the case in which is considered the economic value, given by the specific weight. The application of this technique so far consisted in the adjustment of linear regressions of the production of each cultivar on the environmental index. We will deepen its application to improvement plans, so to cases in which we have a wide number of years of trials done. We will discuss the problems related to the convergence of the zig-zag algorithm, presenting a validation of adjustments through a linear model for the residues. We will emphasise the concept of the multiplicative approximate normality through a set of simulations which will allow us to achieve important conclusions concerning the practical application

Joint regression analysis applied to genotype stability evaluation over years

Most genotype differences connected with yield stability are due to genotype environment interaction. The presence and dimension of this interaction are the factors that determine the performance of genotypes in distinct environments. The environmental factors, like annual rainfall, temperature, diseases or soil fertility, can only explain part of this interaction. Many statistical tools have been developed with the aim to explain the information contained in the GE interaction data matrix. In our work we use the Joint Regression Analysis (JRA), the Zig-Zag Algorithm to estimate the regression coefficients and the multiple comparison tests of Scheffé, Tukey and Bonferroni. We point out not just the limitations of the JRA when used year by year, but also genotype selection advantage from general JRA over years. Data of the Portuguese Plant Breeding Board were used to carry the year and over years analyses of yielding stability of 22 different genotypes of oat (Avena sativa L.) at six different locations in the years 2002, 2003 and 2004.info:eu-repo/semantics/publishedVersio

Modeling non-life insurance price for risk without historical information

How should an insurer price a risk for which there is no history? This work intends to show, step by step, which main mechanisms are needed to capture the tariff model of another insurance company minimizing the risk involved. The document generally deals with the price-making mechanisms in non-life insurance through the GLM regression models — Generalized Linear Model, more precisely the Poisson, Gamma and Tweedie models. Given the complexity of the application of these models in experimental design, it is studied a simpler way to characterize the rate, namely considering the Box–Cox transformation with SUR — Seemingly Unrelated Regression. An orthogonal experimental design to collect information is also presented as well as an application of these methods in the motor industry considering different companies.info:eu-repo/semantics/publishedVersio