An EWMA chart for monitoring process mean

In the statistical process control literature, there exists several improved quality control charts based on cost-effective sampling schemes, including the ranked set sampling (RSS) and median RSS (MRSS). A generalized cost-effective RSS scheme has been recently introduced for efficiently estimating the population mean, namely varied L RSS (VLRSS). In this article, we propose a new exponentially weighted moving average (EWMA) control chart for monitoring the process mean using VLRSS, named the EWMA-VLRSS chart, under both perfect and imperfect rankings. The EWMA-VLRSS chart encompasses the existing EWMA charts based on RSS and MRSS (named the EWMA-RSS and EWMA-MRSS charts). We use extensive Monte Carlo simulations to compute the run length characteristics of the EWMA-VLRSS chart. The proposed chart is then compared with the existing EWMA charts. It is found that, with either perfect or imperfect rankings, the EWMA-VLRSS chart is more sensitive than the EWMA-RSS and EWMA-MRSS charts in detecting small to large shifts in the process mean. A real dataset is also used to explain the working of the EWMA-VLRSS chart.     

Education Enrollment Level and Income Inequality: A Case of SAARC Economies

This article introduces ‘critical open-mindedness’ as a new sociological construct, which can be employed particularly in the studies of social attitudes and attitude change, social values, social identities, cross-cultural relations and social discrimination. By drawing on the data collected through the 2005 World Values Survey in Australia, we have operationalized the construct into an integrative social index, called ‘critical open-mindedness index’ consisting of five dimensional composite indicators (CIs; i.e. the social, political, cultural, economic, and environmental). We have adopted an integrative approach to constructing these composite indicators in which we pragmatically select and incorporate a variety of techniques with the purpose of maximizing the validity of the end results. The findings with respect to Australians’ critical open-mindedness, both in general and in reference to its five dimensions are discussed. We have also developed and examined a social psychological index of ‘socio-cognitive open-mindedness’ inspired by a number of commonly used international scales and by drawing on the same dataset. We have shown that these two types of open-mindedness are qualitatively different. Our analysis does not support the idea that individuals’ social psychological open-mindedness determines their critical open-mindedness. It is rather the opposite which is the case.     

Applications of Machine Learning Methods to Predict Readmission and Length-of-Stay for Homeless Families: The Case of Win Shelters in New York City

ABSTRACT

New York City faces the challenge of an ever-increasing homeless population with almost 60,000 people currently living in city shelters. In 2015, approximately 25% of families stayed longer than nine months in a shelter, and 17% of families with children that exited a homeless shelter returned to the shelter system within 30 days of leaving. This suggests that “long-term” shelter residents and those that re-enter shelters contribute significantly to the rise of the homeless population living in city shelters and indicate systemic challenges to finding adequate permanent housing. This article focuses on our preliminary work with Win (Women-in-Need) shelters to understand the factors that predict readmission and length-of-stay of homeless families. We create a unified, comprehensive database of the homeless population being served by Win shelters, accounting for more than 6,000 homeless families. We apply logistic regression models and an unsupervised clustering algorithm to identify predictors of re-entry and long-term length-of-stay. Citizenship, age, medical conditions, employment, and history of foster care or shelter stays as a child are found to be significant predictors. The results of the K-means clustering identify three primary groups, consistent with previous typologies characterized by transitionally homeless, episodically homeless, and chronically homeless.     

Comparisons of decision tree methods using water data

This article demonstrates the application of classification trees (decision trees), logistic regression (LR), and linear discriminant function (LDR) to classify data of water quality (i.e., whether the water is fit for drinking on not fit for drinking). The data on water quality were obtained from Pakistan Council of Research in Water Resources (PCRWR) for two cities of Pakistan—one representing industrial environment (Sialkot) and the other one representing non-industrial environment (Narowal). To classify data on water quality, three statistical tools were employed—the Decision Tree methodology using Gini Index, LR, and LDA—using R software library. The results obtained by the said three techniques were compared using misclassification rates (a model with minimum value of misclassification rate is better). It was witnessed that LR performed well than the other two techniques while the Decision trees and LDA performed equally well. But for illustration purposes decision trees technique is comparatively easy to draw and interpret.