Monitoring non-parametric profiles using adaptive EWMA control chart

To monitor the quality of a process in statistical process control (SPC), considering a functional relationship between a dependent variable and one or more independent variables (which is denoted as profile monitoring) is becoming an increasingly common approach. Most of the studies in the SPC literature considered parametric approaches in which the functional relationship has the same form in the in-control (IC) and out-of-control (OC) situations. Non-parametric profiles, which have a different functional relationship in the OC conditions are very common. This paper designs a novel control chart to monitor not only the regression parameters but also the variation of the profiles in Phase II applications using an adaptive approach. Adaptive control charts adjust the final statistic with regard to information of the previous samples. The proposed method considers the relative distance of the chart statistic to the control limits as a tendency index and provides some outcomes about the process condition. The results of Monte Carlo simulations show the superiority of the proposed monitoring scheme in comparison with the common non-parametric control charts. 2022, The Author(s).The publication of this article was funded by Qatar National Library.Scopu

Relationship Between the California Drought and Almond Demand

Areas of California\u27s Central Valley are sinking at rates up to 1 foot per year due to subsidence caused, in part, by the state\u27s years-long drought, challenging growers to locate additional water sources for their crops. Supply and demand theory guided this correlational study. The purpose of the study was to examine the financial impact of drought on almond demand. This study included annualized historical almond industry data for the United States (N = 97), downloaded from a United States Department of Agriculture database. The results of multiple linear regression analysis indicated that the model was capable of predicting almond demand, F(3,92) = 483.579, p \u3c .001, R2 = .940. Both supply and price were statistically significant in the final model, with supply (p \u3c .001) accounting for a higher contribution to the model than price (p = .015). Fine effect\u27s contribution (p = .267) to the model was not statistically significant. The results of this study could enable almond industry leaders to increase profit margins through market predictability understanding and mitigate fiscal risks associated with variable labor and groundwater pumping costs. The implications for positive social change include the potential to restore employment opportunities, stabilize migratory worker prospects, and reduce water utilization to preserve natural resources

Birth registration and educational access in Sub-Saharan Africa: The case for an explanatory spatial research design

In 2019, the United Nations Children’s Fund (UNICEF) estimated the global number of children under the age of 5 without birth registrations at 166 million, with the largest share being present in Sub-Saharan Africa. As the author witnessed firsthand while working in Cameroon, the lack of birth registration documentation (i.e. birth certificates) precluded students from progressing from primary to secondary education. Struck by this example of social exclusion, the purpose of this study was to examine the extent to which birth registration acted as a barrier to educational access in primary and secondary education systems elsewhere across Sub-Saharan Africa. An interdisciplinary conceptual framework revealed a gap in academic literature with only a few studies having explored the relationship between birth registration and access to education in a regional context. This study filled such gap by advancing an innovative explanatory spatial mixed methods research design to analyze secondary data from UNICEF and the United Nations Educational, Scientific, and Cultural Organization (UNESCO). This unique design consisted of an initial quantitative multiple regression analysis followed by a spatial autocorrelation analysis, using geographic information systems (GIS), to explain the geography of the initial results. Results from this pragmatic research approach, outlined in a journal-article dissertation format, were intended to be made useful for researchers and policymakers alike. Noteworthy for the former audience, the quantitative strand found that while birth registration was not a significant predictor of access to education at any level of schooling, there were significant effects of gross domestic product (GDP) per capita and rurality on educational access (Article #1). For the latter readers, choropleth maps of birth registration revealed some areas of neighboring countries with similar levels of low registration despite the absence of statistical clustering. However, access to education demonstrated statistically significant cluster patterns (p\u3c0.05) at the primary and lower-secondary levels, offering organizations like UNICEF and UNESCO noteworthy findings that could better inform policy interventions (Article #2). Finally, the author integrated both data strands using a multivariate cluster analysis in the ArcGIS platform, providing a compelling argument for the use of spatial mixed methods in educational policy research (Article #3)