There is extensive research in the monitoring of a process whose characteristics are represented as profiles. However, most current techniques require all observations from each profile to determine the process state. We study the use of a Shewhart chart based on a Gaussian process model with heteroscedasticity for the online monitoring of profiles, while these are being developed, where the central line is the predictive mean and the control limits are based on the prediction band. The advantage is that we do not have to wait until a profile ends to make process corrections. Our results indicate that our method is effective.

The topic of anomaly detection in networks has attracted a lot of attention in recent years, especially with the rise of connected devices and social networks. Anomaly detection spans a wide range of applications, from detecting terrorist cells in counter-terrorism efforts to identifying unexpected mutations during ribonucleic acid transcription. Fittingly, numerous algorithmic techniques for anomaly detection have been introduced. However, to date, little work has been done to evaluate these algorithms from a statistical perspective. This work is aimed at addressing this gap in the literature by carrying out statistical evaluation of a suite of popular spectral methods for anomaly detection in networks. Our investigation on the statistical properties of these algorithms reveals several important and critical shortcomings that we make methodological improvements to address. Further, we carry out a performance evaluation of these algorithms using simulated networks and extend the methods from binary to count networks.

Standard statistical quality control text books (e.g., Montgomery 2015 Montgomery, D. C. 2015. Introduction to statistical quality control, 7th ed. Hoboken, NJ : John Wiley and Sons. [Google Scholar] ) discuss the differences between Phase I and Phase II control charts. In Phase I, control charts are more of an exploratory data analytical tool whose purpose is to generate the control limits for Phase II. Phase II is the true control procedure, which may be viewed as a series of hypothesis tests. The null hypothesis is that the process is in-control, and the alternative is that it is out-of-control. The typical presentation of Phase I is a preset number of rational subgroups. Such an approach fails to address the tension that exists between having enough information to generate reliable control limits and being able to start active control of the process. Vining ( 1998 Vining, G. G. 1998. Statistical methods for engineers. Belmont, CA : Duxbury Press. [Google Scholar] ; 2009 Vining, G. 2009. Technical Advice: Phase I and Phase II control charts. Quality Engineering 21: 478–479. [Taylor & Francis Online], [Web of Science ®] [Google Scholar] ) outline an iterative approach that he developed in the early 1980s when he was employed by the Faber-Castell Corp. This article discusses the differences between Phase I and Phase II control charts and the impact of the number of rational subgroups in the Phase I study upon the quality of the resulting control limits. It then presents two brief case studies that illustrate the iterative approach for the basic Shewhart and R charts.

In practice, engineers seek to find reasonable solutions for complex and unstructured problems, which are common in many areas. The workable solutions for these problems are never a one-shot experiment and data analysis procedure. Rather, the proper solution for these problems requires an inductive-deductive process which involves a series of experiments. To teach engineers the sequential learning strategy in solving complex problems, this article presents a case study on the startup of an ethanol–water distillation column that illustrates the scientific process of response surface methodology. The goal of this experiment is generally to find a good, robust solution that produces high grade concentration of ethanol with maximum profit. This case illustrates the sequential application of response surface methodology and consists of an initial fractional factorial design, a steepest ascent design, a full factorial design, and a central composite face-centered cube design. The analysis of the data in the previous steps gives engineers a guidance about the design of experiment in the next step. This study uses the desirability function approach to obtain a compromise optimization between the concentration of ethanol and the profit, which gives a robust solution to the complex problem. Finally, we conduct appropriate confirmation experiments to verify the optimization results. The case study emphasizes the importance of sequential nature and provides a useful guidance for engineers to solve complex problems.

Universities had to scramble to arrange and redesign their teaching strategies due to the pandemic caused by the COVID-19 outbreak during the first semester of 2020. A sudden shift occurred from on-site to remote teaching, accompanied by a change in students learning habits. Such changes were committed to accomplishing the expected outcomes and competencies for the courses in progress. Higher education practitioners deemed it necessary to assess the validity and reliability of the newly applied teaching and evaluation approaches to ascertain the extent of fulfillment of students' achievement. This study uses the analysis of variance (ANOVA) for assessing expectation fulfillment and outcomes of online teaching during the 2020 lockdown. This work compares the outcomes between remote learning during the confinement period and the previous semesters amidst on-site teaching. The results draw on the instructor performance under the lockdown redesigned environment and the students' perceptions of their learning outcomes. The procedure has been applied to the subjects Simulation (SIM), Applied Statistics (EDA) taught at the Faculty of Engineering of the Universidad de Piura (UDEP, Peru), and Strength of materials (RdM), imparted at the ETSI Caminos, Canales y Puertos of the Universidad Politécnica de Madrid (UPM, Spain). The findings show the impact on some students' expectancies by the change to remote teaching and some improvement opportunities for future online teaching scenarios.