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In this paper we discuss the continuation-ratio model for ordinal data. This particular type of model is to model the probability of one particular category given the categories proceeding this one. It can be shown that estimation of the continuation-ratio model parameters can be done efficiently by using the techniques in fitting the binary data models. In this way, one does not have to estimate the cut-point parameters as in the cumulative probability models. A Bayesian approach with the use of Gibbs sampler is adopted in this paper. The adaptive rejection sampling method proposed by Gilks and Wild is used. The adaptive rejection sampling (ARS) algorithm is an efficient and direct method to sample from complicated log-concave densities often found in many Gibbs sampling scheme. We applied this model to analyse data obtained from experiments about quality of telephone connection conducted by British Telecom (BT) Laboratory. The final results are satisfactory. [ABSTRACT FROM AUTHOR]

One of the most important issues in the world of financial management is investment assurance for legal and rightful persons. The existence of suitable standards and instruments to evaluate financial conditions and companies situations can help investors to under take their decisions making. Financial corruption and bankruptcy bear extensive costs which cause lasting damages to country's economy. One of the ways to prevent financial corruption is to forecast financial distress. In this paper which uses the correlation research methodology employing Artificial Neural Networks (ANN), the financial corruption of the companies has been forecasted. The main aim of this research is to compare ANN and Multi Decimal Analysis (MDA) models in bankruptcy forecasting field. In order to study the effect of sampling differences on forecasting, the absolute validation method has been used. Dual Comparison Test was used to test three hypothesises introduced in this research. The results obtained from the models based on the data from 80 accepted companies in Tehran Stock Exchange show that in forecasting financial distress, the ANN model is more accurate than the MDA model. [ABSTRACT FROM AUTHOR]

One of the main principles in a design of experiment is to use blocking factors whenever it is possible. On the other hand, if the blocking information is not precisely defined and subjective in nature, it is usually discarded. This paper introduces a new design that utilizes available subjective information on experimental units to create artificial blocking factors and develops a rank-sum test to test the difference between two population medians. [ABSTRACT FROM AUTHOR]

Under Skewed Class Distribution Data (SCDD) Real-world applications often involve highly skewed data in decision outcomes. Such a highly skewed class distribution problem, if not properly addressed, would imperil the resulting learning effectiveness. We use one hospital Central Venous Catheter Insertion data for the training data. Among them find CVP-tip attribute are skewed class distribution. The paper uses under-sampling over-sampling and cost-sensitivity to solve SCDD problem. And then use C4.5 algorithms to build up classification model for the central venous catheter insertion. Verify via the expert that use this model can effectively help the medical expert to extract out it about Central Venous Catheter Insertion good knowledge rules. [ABSTRACT FROM AUTHOR]

Chapter 6 of the book "Stop Faking It! Finally Understanding Science So You Can Teach It: Math" is presented. It explores the uses of word equation for translating a word problem into a math equation and create an equation that represents sampling a population. It elaborates the meaning of word problem through giving examples, illustrations and guideposts. Moreover, it stresses the roles of fractions in estimating word problem.

In this paper, we apply a dynamic microsimulation approach, which allows us to examine the evolution of the system as a whole and at the same time to focus our attention on the different typologies of workers and pensioners. The latter objective is achieved by simulating individual reactions to systemic changes, while taking into account the regional dimensions. This technique also enables us to perform a general micro-analysis of the effects of past reforms on family pension-income distribution and average individual pension-benefits. The analytical framework used is the dynamic microsimulation model MIND, jointly developed by the University of Parma and Pisa, that incorporates behavioural analysis of individual choices of retirement age, derived from the Stock-Wise [24] option value model. We also perform sensitivity analysis on the model. This represents a valuable technique for treating uncertainty in input variables and for testing the robustness of the simulation results to possible changes in the macroeconomic scenario. In particular, we measure the economic impact resulting from alternative values of the income growth rate and the real interest rate as well as the effect of different distributions of the education degree reached by the individuals. [ABSTRACT FROM AUTHOR]