Hypothesis-testing Models Research Articles

Rainy and dry seasons impact on electricity demand in Indonesia

Electricity consumption has become an integral part of daily life and is pivotal in supporting various aspects of human life. North Aceh Regency, a tropical region in Indonesia, experiences significant seasonal fluctuations between the rainy and dry seasons. This research aims to investigate and analyze the impact of these seasonal differences on electricity consumption patterns by consumers in the region using the IBM SPSS statistical method. Monthly electricity consumption data from consumers in North Aceh Regency over a specific period were collected and analyzed using IBM SPSS software. Descriptive statistical analysis, hypothesis testing, and regression models were employed to identify significant differences in electricity consumption between the rainy and dry seasons and to understand the factors influencing consumption patterns. The results of the analysis indicate a significant difference in electricity consumption between the rainy and dry seasons in North Aceh Regency. The dry season shows an increase in electricity consumption, possibly related to factors such as the use of air conditioning and additional lighting.

Epidemiological and clinical trends of visceral leishmaniasis in Portugal: retrospective analysis of cases diagnosed in public hospitals between 2010 and 2020

BackgroundLeishmania infantum is endemic in the Mediterranean region, presenting mostly as visceral leishmaniasis (VL). In Portugal, reporting of VL cases to public health authorities is mandatory, but significant underreporting is likely. This study aimed to describe the epidemiological and clinical aspects of the VL cases diagnosed in hospitals of the Portuguese National Health Service (NHS), between 2010 and 2020.MethodsCollaboration was requested to every hospital of the Portuguese NHS in Mainland Portugal. Cases were screened through a search of diagnostic discharge codes or, if not available, by a search of positive laboratory results for Leishmania infection. Sociodemographic and clinical data was retrieved from medical records. Simultaneously, the National Health authority was contacted to request access to data of notified cases of VL between 2010 and 2020. Descriptive, hypothesis testing and multiple binary logistic regression models were performed.ResultsA total of 221 VL cases were identified. A significant increase in estimated national incidence was seen in the years after 2016 (P = 0.030). VL was predominantly diagnosed in people living with HIV (PLWH) and in children (representing around 60% of the new cases), but the outcome was generally poorer in non-HIV patients with associated immunosuppression, with significantly lower rates of clinical improvement at 7 (P = 0.003) and 30 days (P = 0.008) after treatment. Atypical presentations, with gastrointestinal and/or respiratory involvement, were seen in 8.5% of VL cases. Hemophagocytic lymphohistiocytosis was diagnosed in 40.0% of children under 5 years of age. Only 49.7% of incident VL cases were reported. Simultaneous involvement of the skin was confirmed in 5.9% of patients.ConclusionsVL presents a continuing threat in Portugal, especially to PLWH and children, and an increasing threat to other immunosuppressed groups. Recent increases in incidence should be closely monitored to allow prompt interventions. Programs to control the disease should focus on providing tools for earlier diagnosis and on reducing underreporting and promoting an integrated surveillance of human and animal disease. These data should be combined with asymptomatic infection and vector information, following a One Health approach.Graphical

Statistics in Forest Engineering Degree Programs: What Should be Taught and Who Should Teach It

Quality in teaching Applied Statistics in the Degree in Forestry Engineering requires initially asking why to teach Statistics, who should teach it and what should be taught. This paper aims to answer these questions. Natural discussion groups have been used as a method to achieve the objectives. These groups were formed by professors from the UPM and the National University of Santiago del Estero, in Argentina. The results reached the consensus of the experts on the subject: the need for statistics in the training of forestry engineers and that the main contents should be descriptive statistics, sampling techniques, estimation and hypothesis testing and linear models. Finally, another result was that the teaching staff should be Forestry Engineers to show and involve students in real and practical cases that solve engineering problems that facilitate student learning.

A successive time-to-event model of phyllochron dynamics for hypothesis testing: application to the analysis of genetic and environmental effects in maize

BackgroundThe time between the appearance of successive leaves, or phyllochron, characterizes the vegetative development of annual plants. Hypothesis testing models, which allow the comparison of phyllochrons between genetic groups and/or environmental conditions, are usually based on regression of thermal time on the number of leaves; most of the time a constant leaf appearance rate is assumed. However regression models ignore auto-correlation of the leaf number process and may lead to biased testing procedures. Moreover, the hypothesis of constant leaf appearance rate may be too restrictive.MethodsWe propose a stochastic process model in which emergence of new leaves is considered to result from successive time-to-events. This model provides a flexible and more accurate modeling as well as unbiased testing procedures. It was applied to an original maize dataset collected in the field over three years on plants originating from two divergent selection experiments for flowering time in two maize inbred lines.Results and conclusionWe showed that the main differences in phyllochron were not observed between selection populations but rather between ancestral lines, years of experimentation and leaf ranks. Our results highlight a strong departure from the assumption of a constant leaf appearance rate over a season which could be related to climate variations, even if the impact of individual climate variables could not be clearly determined.

Youth awareness and commitment to global warming risks among university students

BACKGROUND AND OBJECTIVES: Global Warming is expected to induce grave effects around the world. It is predicted that many communities, mostly in the third world, experience numerous consequences because of climate change. Therefore, effective action is needed and universities play a huge role in addressing Global Warming issues and their impacts through research and innovations. Hence, assessing the extent to which African university students understand climate change and its impacts shows the extent of hope in mitigating the climate related risks. This Case study assesses how deep is the knowledge of young educated Africans on Global Warming causes and risks, how committed they are on fighting the issue and applying solutions.METHODS: A hypothesis testing and descriptive statistics models were employed to analyze a qualified data set collected through an online interview survey among young educated Africans taken from a sample of oversea students in Chinese state universities. After a consistency study, Out of 250 questionnaires received, 224 were useful data, and Cronbach’s alpha was 0.75. Participants were taken from 34 different African nationalities. All analyses were conducted using SPSS24.FINDINGS: The results have shown that 39% of the students are very familiar with the topic, and 33% consider it as a known issue and the participants mostly rely on social media to get information about Global Warming. Two hypotheses were tested at 95% level of confidence; the critical statistic was 41.3 against a 51.2 calculated chi square for H01: Young educated Africans don’t have significant knowledge on Global Warming causes. Similarly, a critical value of 36.4 was compared to a calculated chi-square of 40.6 for H02: Young educated Africans don’t have significant knowledge on Global Warming risks. Hence, both hypotheses were rejected. Consequently, it was deduced that young Africans students do have general knowledge on GW risks and causes, even if some deep aspects are still unclear for some. It was revealed that most of the students (81%) are ready to take actions against climate change, and some have even started to do so; besides, they mostly believe that the solutions should come from every human being at any age or background. Using these results, recommendations were given to different stakeholders. CONCLUSION: In Africa, where education about climate change is generally low, the findings in this study may provide all stakeholders with crucial information for better understanding of Global Warming risks and effective response plans. This study has shown that Young educated Africans report a general sense that global warming can negatively impact people’s lives, but relatively few are willing to personally get involved in the response. Therefore, there is need for practical climate change health and safety education leading to improve behaviors among the most remote communities.

Regional Relocation and Phonetic Dialect Markers


 This paper tracks phonological change in the (ING) morpheme in two YouTube personalities over time. Both participants relocated to a different dialect region than their hometowns over the course of their careers, motivating the hypothesis of this paper: geographic relocation is a catalyst for adult accent change. With a longitudinal study method, I selected audio clips from different periods in each YouTuber’s life and collected formant measurements of the targeted words. Based on a Pearson’s correlation analysis and hypothesis testing models, the participants showed statistically significant progression in their speech over time. Additionally, the speakers exhibited audible shifts most likely as an effect of aging. It is inconclusive whether this study’s observations are influenced by the difference in dialect or societal pressures of the relocated locations without further research in the other variables of each regional dialect.

FLYPAPER EFFECT TERHADAP BELANJA DAERAH PADA KABUPATEN BIREUEN

The purpose of this study is to analyze the effect of PAD and DAU on Regional Expenditures and then analyze the flypaper effect. The sampling technique used is purposive sampling with a sample of the Realization Report of the Regional Revenue and Expenditure Budget of Bireuen Regency in 2011-2015. The data used in this research is secondary data. The analytical method used is descriptive statistical test, hypothesis testing and multiple linear regression models. The result of this study is that DAU and PAD simultaneously have a significant effect on Regional Expenditures. Partially, DAU has no significant effect on Regional Expenditures and PAD has a significant effect on Regional Expenditures. During the research period, there was a flypaper effect in Bireuen Regency because the value of DAU to Regional Expenditures was greater than the value of PAD to Regional Expenditures.

The Dynamics of Thefts and Robberies in S\xe3o Paulo\u2019s Metro, Brazil

The aim of this study is to assess the nature and space-temporal dynamics of property crimes (theft and robbery) in transport nodes, namely, metro stations and their immediate surrounding areas. The analysis is based on crime data over São Paulo’s metro system from 2010 to 2017. Drawing from environmental criminology theory, the methodology combines geographical information system (GIS) as well as statistical analysis using hypothesis testing and negative binomial regression models. Results show that thefts happen more often inside the station and robberies outside, with signs of possible interaction between these environments. Crime is often highly concentrated in a few inner city and end stations, but it varies depending on location and time. Future research and policy implications of the results add to the contribution of this current study.

Economic growth and environmental degradation: evidence from the US case environmental Kuznets curve hypothesis with application of decomposition

ABSTRACT This study re-tests the environmetal Kuznets curve (EKC) hypothesis for the US, based on a methodology that differentiates this study from previous empirical studies.To this aim, the per-capita income series (variable) is decomposed into its increases and decreases as two new time series and only one series, which contains income increases, is used. The rationale of this decomposition method is that the EKC hypothesis is originally postulated based on the impacts of income increases on environmental degradation. Therefore, this decomposition may allow us to test the EKC hypothesis more accurately through only income increases in accordance with its original postulation. Following decomposition, the ARDL approach to cointegration is applied between 1990M1 and 2019M7. Empirical findings of decomposed and undecomposed models are exactly opposite to each other. While the undecomposed model does not detect evidence of the EKC hypothesis for the US, the decomposed model strongly does so. This can lead to the interpretation that the decomposed model discovers-detects the existing but concealed validity of the EKC hypothesis, which the undecomposed model is not capable of detecting. Based on this result, this study proposes using this method as well, as an alternative technique for the EKC hypothesis testing models.

Identification of subgroups of terror attacks with shared characteristics for the purpose of preventing mass-casualty attacks: a data-mining approach

Security and intelligence agencies around the world invest considerable resources in preventing terrorist attacks, as these may cause strategic damage, national demoralization, infringement of sovereignty, and government instability. Recently, data-mining techniques have evolved to allow identification of patterns and associations in criminal data that were not apparent using traditional analysis. The aim of this paper is to illustrate how to use interpretable classification algorithms to identify subgroups (“patterns”) of terrorist incidents that share common characteristics and that result in mass fatalities. This approach can produce insights far beyond those of conventional macro-level studies that use hypothesis-testing and regression models. In addition to this methodological contribution, from a practical perspective, exploring the characteristics identified in the “patterns” can lead to prevention strategies, such as alteration of the physical or systemic environment. This is in line with situational crime prevention (SCP) theory. We apply our methodology to the Global Terrorism Database (GTD). We present three examples in which terror attacks that are described by a particular pattern (set of characteristics) resulted in a high probability of mass casualties, while attacks that differ in just one of these characteristics (i.e., month of attack, geographical area targeted, or type of attack) resulted in far fewer casualties. We propose exploration of the differentiating characteristic as a means of reducing the probability of mass-fatality terrorist incidents.

An Integrated Quantitative Approach to Acceptance Testing and Related Decisions

AbstractThis paper provides an integrated, analytical framework (and associated tools) for determining the scope of an acceptance test, designing acceptance tests, and for making acceptance decisions. A decision tree model is developed that may be used to determine the value of different acceptance test designs. Hypothesis testing models are developed that may be used to determine the performance threshold for testing, given a desired confidence of failing a bad system (test “Power”), and the design margin required to have a given confidence of passing a good system (test “Confidence”). These two models are then integrated into a single model that may be used to establish a test design (in terms of an acceptance threshold and the number of measurements) that achieves a desired test Power and Confidence. Finally a decision tree model is developed for the system acceptance decision based on the results of a given acceptance test.

Case Article—ABCtronics: Manufacturing, Quality Control, and Client Interfaces

Teaching statistics in an undergraduate or a graduate business administration course, business analytics course, or engineering program remains a challenging task for the instructors. Often students fail to comprehend the real-life application of statistical concepts with the help of disconnected textbook examples. This failure inspires scholars to develop case studies on statistical theories in real-life industry backgrounds or realistic industrial settings. Recognizing the students’ difficulty to understand the diverse statistical concepts in a realistic setting, we design a case study in an industrial background to facilitate students’ learning. It is constructed by drawing inspiration from real-life business scenarios in the context of the integrated chip manufacturing industry. It helps to bridge the gap between theoretical understanding and practical application. The case starts with introducing various probability distributions and gradually incorporates intricate concepts of statistics like sampling distribution, hypothesis testing, and regression models in realistic scenarios. Each assignment question enables the teacher to engage in the in-depth discussion regarding the applications of different statistical concepts about business problems of integrated chip manufacturing. Also, it involves the application of the software tools for solving statistical problems. This case article demonstrates different usages of the case, ranging from a common thread for classroom discussion throughout the course to a class reading for providing a holistic approach to understanding and applying different statistical concepts in real-life scenarios by the end of the course.Case Teaching Note: Interested Instructors please see the Instructor Materials page for access to the restricted materials. To maintain the integrity and usefulness of cases published in ITE, unapproved distribution of the case teaching notes and other restricted materials to any other party is prohibited.

Analytic Queries over Geospatial Time-Series Data Using Distributed Hash Tables

As remote sensing equipment and networked observational devices continue to proliferate, their corresponding data volumes have surpassed the storage and processing capabilities of commodity computing hardware. This trend has led to the development of distributed storage frameworks that incrementally scale out by assimilating resources as necessary. While challenging in its own right, storing and managing voluminous datasets is only the precursor to a broader field of research: extracting insights, relationships, and models from the underlying datasets. The focus of this study is twofold: exploratory and predictive analytics over voluminous, multidimensional datasets in a distributed environment. Both of these types of analysis represent a higher-level abstraction over standard query semantics; rather than indexing every discrete value for subsequent retrieval, our framework autonomously learns the relationships and interactions between dimensions in the dataset and makes the information readily available to users. This functionality includes statistical synopses, correlation analysis, hypothesis testing, probabilistic structures, and predictive models that not only enable the discovery of nuanced relationships between dimensions, but also allow future events and trends to be predicted. The algorithms presented in this work were evaluated empirically on a real-world geospatial time-series dataset in a production environment, and are broadly applicable across other storage frameworks.

Differences between Walking and Bicycling over Time: Implications for Performance Management

This study analyzed travel behavior inventories conducted by the Metropolitan Council in the Minneapolis–Saint Paul, Minnesota, metropolitan area in 2001 and 2010 to illuminate differences between walking and bicycling over time and to illustrate the implications for performance measurement. The study focused on pedestrians and bicyclists: where they went and why, when they traveled, and what factors were associated with their trips. As measured by summer mode share, walking and bicycling increased, but differences between the modes overshadowed their similarities. The results of analysis with descriptive statistics, hypothesis testing, and multinomial logistic models showed that walkers were different from bicyclists, that walking trips were shorter and were made for different purposes, that walking and bicycling trips differed seasonally, and that different factors were associated with the likelihood of a walking or a bicycle trip. The study also showed that the journey to work question in the Census Bureau's American Community Survey tended to underestimate commuting by bicycle by a factor of 1.75 to 3.3, while it slightly overestimated commuting by walking. The increase in mode share was greater for walking than for bicycling. Walking and bicycling remained mainly urban transportation options. Older age reduced the likelihood of biking trips more than it did of walking trips, and biking remained associated with gender but walking did not. These differences called into question the common practice of treating nonmotorized transportation as a single mode. Managers can use these results to develop performance measures to track progress toward system goals in a way that address the unique and different needs of pedestrians and bicyclists.

The Influence of Family Factors on Smoking Behavior in Turkey

Aim: The goal of this study is to specify the risks, family and environmental factors affecting smoking behavior and develop suggestions for Turkish individuals by considering sibling data. Materials and Methods: The data was collected by voluntary senior year students attending Kırıkkale University, Department of Statistics. The sample of 751 families was selected from families with at least two children. Each sibling’s socio-demographic information and behavioral phenotypes were collected using a survey from both siblings. We selected one of siblings randomly as ‘sibling1’ and defined the other sibling as ‘sibling2’. Hypothesis testing and multivariable clustered logistic regression models were used to evaluate the data and find the optimum model by using dependent sibling data. Results: Out of 1502 (751 pairs) siblings 843 (56.1%) were males, 659 (43.9%) were females. According to the survey results, 508 of the males (67.7%) and 242 of the females (32.3%) were smokers for a month or longer and smoked every day. The risk of smoking was 2.26 times higher in males than in females. Having a smoking sibling increased the risk of smoking 1.95 times, alcohol using increased the risk 2.11 times. We found that when the age difference between siblings is 0-7 years, having a same sex sibling who smokes increases one’s risk 4.7 times in females and 5 times in males; when the siblings are of different sexes, according to these age differences Conclusion: The survey showed that the gender and sibling’s and parent’s smoking both play a significant role on smoking behavior. But children seem to learn smoking from their siblings more than from parents. Having same sex sibling who smokes plays significant role in smoking behavior for both males and females.

Modeling cross-situational word-referent learning: prior questions.

Both adults and young children possess powerful statistical computation capabilities--they can infer the referent of a word from highly ambiguous contexts involving many words and many referents by aggregating cross-situational statistical information across contexts. This ability has been explained by models of hypothesis testing and by models of associative learning. This article describes a series of simulation studies and analyses designed to understand the different learning mechanisms posited by the 2 classes of models and their relation to each other. Variants of a hypothesis-testing model and a simple or dumb associative mechanism were examined under different specifications of information selection, computation, and decision. Critically, these 3 components of the models interact in complex ways. The models illustrate a fundamental tradeoff between amount of data input and powerful computations: With the selection of more information, dumb associative models can mimic the powerful learning that is accomplished by hypothesis-testing models with fewer data. However, because of the interactions among the component parts of the models, the associative model can mimic various hypothesis-testing models, producing the same learning patterns but through different internal components. The simulations argue for the importance of a compositional approach to human statistical learning: the experimental decomposition of the processes that contribute to statistical learning in human learners and models with the internal components that can be evaluated independently and together.

Characterizing Single‐Molecule FRET Dynamics with Probability Distribution Analysis

Probability distribution analysis (PDA) is a recently developed statistical tool for predicting the shapes of single-molecule fluorescence resonance energy transfer (smFRET) histograms, which allows the identification of single or multiple static molecular species within a single histogram. We used a generalized PDA method to predict the shapes of FRET histograms for molecules interconverting dynamically between multiple states. This method is tested on a series of model systems, including both static DNA fragments and dynamic DNA hairpins. By fitting the shape of this expected distribution to experimental data, the timescale of hairpin conformational fluctuations can be recovered, in good agreement with earlier published results obtained using different techniques. This method is also applied to studying the conformational fluctuations in the unliganded Klenow fragment (KF) of Escherichia coli DNA polymerase I, which allows both confirmation of the consistency of a simple, two-state kinetic model with the observed smFRET distribution of unliganded KF and extraction of a millisecond fluctuation timescale, in good agreement with rates reported elsewhere. We expect this method to be useful in extracting rates from processes exhibiting dynamic FRET, and in hypothesis-testing models of conformational dynamics against experimental data.

SOCRAnalyses: Implementation and Demonstration of a New Graphical Statistics Educational Toolkit

The web-based, Java-written SOCR (Statistical Online Computational Resource) tools have been utilized in many undergraduate and graduate level statistics courses for seven years now (Dinov 2006; Dinov et al. 2008b). It has been proven that these resources can successfully improve students' learning (Dinov et al. 2008b). Being first published online in 2005, SOCR Analyses is a somewhat new component and it concentrate on data modeling for both parametric and non-parametric data analyses with graphical model diagnostics. One of the main purposes of SOCR Analyses is to facilitate statistical learning for high school and undergraduate students. As we have already implemented SOCR Distributions and Experiments, SOCR Analyses and Charts fulfill the rest of a standard statistics curricula. Currently, there are four core components of SOCR Analyses. Linear models included in SOCR Analyses are simple linear regression, multiple linear regression, one-way and two-way ANOVA. Tests for sample comparisons include t-test in the parametric category. Some examples of SOCR Analyses' in the non-parametric category are Wilcoxon rank sum test, Kruskal-Wallis test, Friedman's test, Kolmogorov-Smirnoff test and Fligner-Killeen test. Hypothesis testing models include contingency table, Friedman's test and Fisher's exact test. The last component of Analyses is a utility for computing sample sizes for normal distribution. In this article, we present the design framework, computational implementation and the utilization of SOCR Analyses.

Schematic Influences on Category Learning and Recognition Memory.

The results from 3 category learning experiments suggest that items are better remembered when they violate a salient knowledge structure such as a rule. The more salient the knowledge structure, the stronger the memory for deviant items. The effect of learning errors on subsequent recognition appears to be mediated through the imposed knowledge structure. The recognition advantage for deviant items extends to unsupervised learning situations. Exemplar-based and hypothesis-testing models cannot account for these results. The authors propose a clustering account in which deviant items are better remembered because they are differentiated from clusters that capture regularities. The function of clusters is akin to that of schemas. Their results and analyses expose connections among research in category learning, schemas, stereotypes, and analogy.

Supporting organizational learning: a comparative approach to evaluation in action research.

For well over a decade, the organizational context of nursing in the United Kingdom has been one of change and reform with a policy shift towards primary and community led health care. Such a major shift in policy provides nurses with an opportunity to re-think their approaches towards the individuals and communities they serve in order to promote self-care, recognize achievable health goals and provide family and community support. Although there is a growing appreciation of action research as an useful approach to this kind of development, it can be difficult to evaluate published studies because of the diverse nature of the literature. This paper will present and evaluate two case studies using a particular conceptual device (The Normative Information Model-based Systems Analysis and Design) in order to explore a possible alternative to the interpretative narrative or positivistic hypothesis-testing models used to report other kinds of nursing research and enlarge the debate about process evaluation.