Amílcar Oliveira

The uniform distribution product: an approach to the (Q,r) inventory model using R

ABSTRACT In this work the probability density function (PDF) for the product of two uniformly distributed random variables is explored under the implementation of a new procedure in R language. Based on the Rohatgi theorem for the theoretical form of the product, different possibilities for the range of values of the limits of both distributions are considered. As an application, the management of a inventory model with the presence of lead-time and uniform demand forecasts is considered. Solution to this model looks up to minimize the total costs through the variables Q (reorder quantity) and r (the reorder point), and not always exists an analytical solution of the problem. We show a graphical procedure for the simulation results and a more exactly analytical solution. Implementation in R is straightforward.

Problem based learning : a brief review

Teaching is a complex mission that requires not only the theoretical knowledge transmission, but furthermore requires to provide the students the necessary skills for solving real problems in their respective professional activities where complex issues and problems must be frequently faced. Over more than twenty years we have been experiencing an increase in scholar failure in the scientific area of mathematics, which means that Teaching Mathematics and related areas can be even a more complex and hard task. Scholar failure is a complex phenomenon that depends on various factors as social factors, scholar factors or biophysical factors. After numerous attempts made in order to reduce scholar failure our goal in this paper is to understand the role of “Problem Based Learning” and how this methodology can contribute to the solution of both: increasing mathematical courses success and increasing skills in the near future professionals in Portugal. Before designing a proposal for applying this technique in o...

Item response theory – A first approach

Research partially supported by National Funds through FCT – Fundacao para a Ciencia e Tecnologia, projects Pest-OE/MAT/UI0006/2013 (CEAUL) and UID/MAT/00297/2013 (CMA/NOVA.ID.FCT).

Evaluation of Kurtosis into the product of two normally distributed variables

Kurtosis (κ) is any measure of the “peakedness” of a distribution of a real-valued random variable. We study the evolution of the Kurtosis for the product of two normally distributed variables. Product of two normal variables is a very common problem for some areas of study, like, physics, economics, psychology, … Normal variables have a constant value for kurtosis (κ = 3), independently of the value of the two parameters: mean and variance. In fact, the excess kurtosis is defined as κ− 3 and the Normal Distribution Kurtosis is zero. The product of two normally distributed variables is a function of the parameters of the two variables and the correlation between then, and the range for kurtosis is in [0, 6] for independent variables and in [0, 12] when correlation between then is allowed.

Linear regression on the characterization of elements of natural origin and possible implications in the use of ground

The observation of certain higher chemical element concentration, such as uranium and radiometric values, in the Alegrete-Assumar region of Portugal, has shown that locally occurrence of radioactive quartzites is responsible for these high values. The geostatistical treatment of exploration data and the crossing of the database with other variables, such as land use, allows one to study how these may affect the human health.

Microarray Experiments on Risk Analysis Using R

The microarray technique is a powerful biotechnological tool, expanding in a interesting way the vision with which issues in medicine are studied. Microarray technology, allows simultaneous evaluation of the expression of thousands of genes in different tissues of a given organism, and in different stages of development or environmental conditions. However, experiments with microarrays are still substantially costly and laborious, and as a consequence, they are usually conducted with relatively small sample sizes, thereby requiring a careful experimental design and statistical analysis. This paper adopts some applications of microarrays in risk analysis using R statistical software.

Tucker3 method in biometrical research: Analysis of experiments with three factors, using R

The present work aims to propose a systematic study and interpretation of a variable response in relation to three factors, using a model of Joint Table Analysis, the Tucker3 model, as well as the joint biplot graph. The proposed method seems efficient and suitable for separating standard technical response, and the pattern of noise contained in a three inputs table, as well as allows its interpretation. The joint plot graph facilitates the study and interpretation of the data structure and provides additional information on these. In our application the aim is to identify the combinations of genotypes, locations and years that contribute or not to a high yield of bean cultivars.