Anam Iqbal

Innovations in technology assist the manufacturing processes in producing high-quality products and, hence, become a greater challenge for quality engineers. Control charts are frequently used to examine production operations and maintain product quality. The traditional charting structures rely on a response variable and do not incorporate any auxiliary data. To resolve this issue, one popular approach is to design charts based on a linear regression model, usually when the response variable shows a symmetric pattern (i.e., normality). The present work intends to propose new generalized linear model (GLM)-based homogeneously weighted moving average (HWMA) and double homogeneously weighted moving average (DHWMA) charting schemes to monitor count processes employing the deviance residuals (DRs) and standardized residuals (SRs) of the Poisson regression model. The symmetric limits of HWMA and DHWMA structures are derived, as SR and DR statistics showed a symmetric pattern. The performance of proposed and established methods (i.e., EWMA charts) is assessed by using run-length characteristics. The results revealed that SR-based schemes have relatively better performance as compared to DR-based schemes. In particular, the proposed SR-DHWMA chart outperforms the other two, namely SR-EWMA and SR-HWMA charts, in detecting shifts. To illustrate the practical features of the study’s proposal, a real application connected to the additive manufacturing process is offered.

The COVID-19 pandemic affected the world through its ability to cause widespread infection. The Middle East including the Kingdom of Saudi Arabia (KSA) has also been hit by the COVID-19 pandemic like the rest of the world. This study aims to examine the relationships between meteorological factors and COVID-19 case counts in three cities of the KSA. The distribution of the COVID-19 case counts was observed for all three cities followed by cross-correlation analysis which was carried out to estimate the lag effects of meteorological factors on COVID-19 case counts. Moreover, the Poisson model and negative binomial (NB) model with their zero-inflated versions (i.e., ZIP and ZINB) were fitted to estimate city-specific impacts of weather variables on confirmed case counts, and the best model is evaluated by comparative analysis for each city. We found significant associations between meteorological factors and COVID-19 case counts in three cities of KSA. We also perceived that the ZINB model was the best fitted for COVID-19 case counts. In this case study, temperature, humidity, and wind speed were the factors that affected COVID-19 case counts. The results can be used to make policies to overcome this pandemic situation in the future such as deploying more resources through testing and tracking in such areas where we observe significantly higher wind speed or higher humidity. Moreover, the selected models can be used for predicting the probability of COVID-19 incidence across various regions.

The control charts are essential instruments that can impart crucial insights to quality controllers for maintaining the productivity of manufacturing processes. The conventional charting designs are based on a single study variable without exerting auxiliary information. Control charts derived from simple linear regression, generally with normality assumption, are one straightforward way of overcoming this difficulty. But, for a count distributed process, the generalized linear model (GLM) based strategy yields better outcomes. Hence, this study presents GLM‐based progressive mean (PM) and double progressive mean (DPM) charting structures by employing the standardized residuals of the Poisson regression model. The functioning of the suggested and existing schemes (i.e., SR‐EWMA) is investigated in the matter of run length distributions. The comparative analysis demonstrated that PM structures based on standardized residuals (i.e., SR‐PM and SR‐DPM) outperform the existing counterpart (i.e., SR‐EWMA). Specifically, the SR‐DPM chart is found to be more productive in identifying increasing alterations in the process mean. Finally, a case study about a 3D manufacturing procedure is presented to accentuate the significance of the suggested methods.

In this paper, single-phase mixture regression cum ratio estimators are presented by utilizing auxiliary variables and auxiliary attributes simultaneously under stratified random sampling. Special cases of these estimators are discussed and further mean square errors are extracted mathematically. Also, to observe the properties of proposed estimators, simulation technique is used which shows that the distribution of the proposed estimators is approximately normal. To differentiate the performance of the proposed estimators, an empirical study has been conducted by incorporating quantitative and qualitative characteristics in the form of auxiliary attributes and variables simultaneously. Comparisons are made with single-phase mixture regression cum ratio estimators under simple random sampling. It has been found that the mixture regression cum ratio estimators employing multiple auxiliary variables and attributes, simultaneously, under stratified random sampling are more efficient than mixture regression cum ratio estimator under simple random sampling.

Inflation is one of the serious economic indicators in Pakistan. Inflation can be crawling, walking, running, hyper and stagflation according to nature. To model monthly inflation rate in Pakistan periodogram analysis and frequency domain analysis which is also known as Fourier analysis or spectral analysis is used. After analyzing the data, inflation cycle length is observed and appropriate Fourier series models are fitted to the data. Monthly inflation rate is also analyzed by Auto Regressive Integrated Moving average (ARIMA). Further, models are compared and it is found that Fourier series models are more suitable to forecast inflation rate of Pakistan.