A process capability index for zero-inflated processes

The proportion of zero defect (ZD) outputs is as an integral characteristic of a zero-inflated (ZI) process or high quality process. Different ZI processes can almost equally satisfy the same USL of number of defects but they can produce substantially different proportions of ZD products. The application of conventional method for process capability evaluation fails to discriminate these processes because in the conventional method, the process capability is evaluated taking into consideration the USL of number of defects only. In this paper, a new measure of process capability for ZI processes is proposed that can truly discriminate different ZI processes taking into account the USL of number of defects as well as the proportion of ZD units produced in these processes. In the proposed approach, at first a measure of process capability index (PCI) with respect to the USL is computed, and then the overall PCI is obtained by multiplying it with an appropriately defined multiplying factor. A real-life application is presented

Fertility in South Dublin a Century Ago: A First Look

Ireland's relatively late and feeble fertility transition remains poorly-understood. The leading explanations stress the role of Catholicism and a conservative social ethos. This paper reports the first results from a project that uses new samples from the 1911 census of Ireland to study fertility in Dublin and Belfast. Our larger project aims to use the extensive literature on the fertility transition elsewhere in Europe to refine and test leading hypotheses in their Irish context. The present paper uses a sample from the Dublin suburb of Pembroke to take a first look at the questions, data, and methods. This sample is much larger than those used in previous studies of Irish fertility, and is the first from an urban area. We find considerable support for the role of religion, networks, and other factors stressed in the literature on the fertility transition, but the data also show a role for the social-class effects downplayed in recent discussions.Ireland, Fertility, Demography

Analysis of acoustic emission data for bearings subject to unbalance

Acoustic Emission (AE) is an effective nondestructive method for investigating the behavior of materials under stress. In recent decades, AE applications in structural health monitoring have been extended to other areas such as rotating machineries and cutting tools. This research investigates the application of acoustic emission data for unbalance analysis and detection in rotary systems. The AE parameter of interest in this study is a discrete variable that covers the significance of count, duration and amplitude of AE signals. A statistical model based on Zero-Inflated Poisson (ZIP) regression is proposed to handle over-dispersion and excess zeros of the counting data. The ZIP model indicates that faulty bearings can generate more transient wave in the AE waveform. Control charts can easily detect the faulty bearing using the parameters of the ZIP model. Categorical data analysis based on generalized linear models (GLM) is also presented. The results demonstrate the significance of the couple unbalance

Marketing the Mountain State: A large N study of user engagement on Twitter

Much of the evolving research on the use of social media in destination marketing emphasizes how information diffusion influences the reputational image of place. The present study uses Twitter data to focus on the relative differences in user engagement across discrete account types. Specifically, this is done to examine how the official destination marketing organization of Montana—the Montana Office of Tourism (MTOT)—performs relative to other account types. Several regression analyses conducted on Twitter data associated with an ongoing MTOT place branding campaign reveal that tweets sent from ‘official’ accounts are more likely to be retweeted, and are estimated to receive more total retweets. The inclusion of a URL or mention, and the number of followers an account has, are also predicted to positively impact retweets. These results will be useful for economic development professionals working in state and local governments, tourism and marketing companies and nonprofits, university researchers, and community members who seek to understand how destination marketing is being conducted. Those interested in methodology and data collection techniques for Twitter-based research, and data manipulation in Python may also benefit from the study

On Shewhart Control Charts for Zero-Truncated Negative Binomial Distributions

The negative binomial distribution (NBD) is extensively used for thedescription of data too heterogeneous to be fitted by Poissondistribution. Observed samples, however may be truncated, in thesense that the number of individuals falling into zero class cannot bedetermined, or the observational apparatus becomes active when atleast one event occurs. Chakraborty and Kakoty (1987) andChakraborty and Singh (1990) have constructed CUSUM andShewhart charts for zero-truncated Poisson distribution respectively.Recently, Chakraborty and Khurshid (2011 a, b) have constructedCUSUM charts for zero-truncated binomial distribution and doublytruncated binomial distribution respectively. Apparently, very littlework has specifically addressed control charts for the NBD (see, forexample, Kaminsky et al., 1992; Ma and Zhang, 1995; Hoffman, 2003;Schwertman. 2005).The purpose of this paper is to construct Shewhart control chartsfor zero-truncated negative binomial distribution (ZTNBD). Formulaefor the Average run length (ARL) of the charts are derived and studiedfor different values of the parameters of the distribution. OC curvesare also drawn

The Effects of Inaccurate and Missing Highway-Rail Grade Crossing Inventory Data on Crash and Severity Model Estimation and Prediction

Highway-Rail Grade Crossings (HRGCs) present a significant safety risk to motorists, pedestrians, and train passengers as they are intersections where roads and railways intersect. Every year, HRGCs in the US experience a high number of crashes leading to injuries and fatalities. Estimations of crash and severity models for HRGCs provide insights into safety and mitigation of the risk posed by such incidents. The accuracy of these models plays a vital role in predicting future crashes at these crossings, enabling necessary safety measures to be taken proactively. In the United States, most of these models rely on the Federal Railroad Administration\u27s (FRA) HRGCs inventory database, which serves as the primary source of information for these models. However, errors or incomplete information in this database can significantly impact the accuracy of the estimated crash model parameters and subsequent crash predictions. This study examined the potential differences in expected number of crashes and severity obtained from the Federal Railroad Administration\u27s (FRA) 2020 Accident Prediction and Severity (APS) model when using two different input datasets for 560 HRGCs in Nebraska. The first dataset was the unaltered, original FRA HRGCs inventory dataset, while the second was a field-validated inventory dataset, specifically for those 560 HRGCs. The results showed statistically significant differences in the expected number of crashes and severity predictions using the two different input datasets. Furthermore, to understand how data inaccuracy impacts model estimation for crash frequency and severity prediction, two new zero-inflated negative binomial models for crash prediction and two ordered probit models for crash severity, were estimated based on the two datasets. The analysis revealed significant differences in estimated parameters’ coefficients values of the base and comparison models, and different crash-risk rankings were obtained based on the two datasets. The results emphasize the importance of obtaining accurate and complete inventory data when developing HRGCs crash and severity models to improve their precision and enhance their ability to predict and prevent crashes. Advisor: Aemal J. Khatta

Phase II control charts for autocorrelated processes

A large amount of SPC procedures are based on the assumption that the process subject to monitoring consists of independent observations. Chemical processes as well as many non-industrial processes exhibit autocorrelation, for which the above-mentioned control procedures are not suitable. This paper proposes a Phase II control procedure for autocorrelated and possibly locally stationary processes. A time-varying autoregressive (AR) model is proposed, which is capable of dealing with the autocorrelation as well as with local non-stationarities of the temporal process. Such non-stationarities are induced by the time-varying nature of the AR coefficients. The model is optimized during Phase I when it is assured that the process is in control and as a result the model describes accurately the process. The Phase II proposed control procedure is based on a comparison of the current time series model with an alternative model, measuring deviations from it. This comparison is carried out using Bayes factors, which help to establish the in-control or out-of-control state of the process in Phase II. Using the threshold rules of the Bayes factors, we propose a binomial-type control procedure for the monitoring of the process. The methodology of this paper is illustrated using two data sets consisting of temperature measurements at two different stages in the manufacturing of a plastic mould