779 resultados para Aiken Technical College--Statistics


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The majority of sugar mill locomotives are equipped with GPS devices from which locomotive position data is stored. Locomotive run information (e.g. start times, run destinations and activities) is electronically stored in software called TOTools. The latest software development allows TOTools to interpret historical GPS information by combining this data with run information recorded in TOTools and geographic information from a GIS application called MapInfo. As a result, TOTools is capable of summarising run activity details such as run start and finish times and shunt activities with great accuracy. This paper presents 15 reports developed to summarise run activities and speed information. The reports will be of use pre-season to assist in developing the next year's schedule and for determining priorities for investment in the track infrastructure. They will also be of benefit during the season to closely monitor locomotive run performance against the existing schedule.

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Experts are increasingly being called upon to quantify their knowledge, particularly in situations where data is not yet available or of limited relevance. In many cases this involves asking experts to estimate probabilities. For example experts, in ecology or related fields, might be called upon to estimate probabilities of incidence or abundance of species, and how they relate to environmental factors. Although many ecologists undergo some training in statistics at undergraduate and postgraduate levels, this does not necessarily focus on interpretations of probabilities. More accurate elicitation can be obtained by training experts prior to elicitation, and if necessary tailoring elicitation to address the expert’s strengths and weaknesses. Here we address the first step of diagnosing conceptual understanding of probabilities. We refer to the psychological literature which identifies several common biases or fallacies that arise during elicitation. These form the basis for developing a diagnostic questionnaire, as a tool for supporting accurate elicitation, particularly when several experts or elicitors are involved. We report on a qualitative assessment of results from a pilot of this questionnaire. These results raise several implications for training experts, not only prior to elicitation, but more strategically by targeting them whilst still undergraduate or postgraduate students.

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This study models the joint production of desirable and undesirable output production (that is, CO2 emissions) of airlines. The Malmquist–Luenberger productivity index is employed to measure productivity growth when undesirable output production is incorporated into the production model. The results show that pollution abatement activities of airlines lowers productivity growth, which suggests that the traditional approach of measuring productivity growth, which ignores CO2 emissions, overstates ‘true’ productivity growth. The reliability of the results is also tested and verified using confidence intervals based on bootstrapping.

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Biomass is an important energy resource for producing bioenergy and growing the global economy whilst minimising greenhouse gas emissions. Many countries, like Australia have a huge amount of biomass with the potential for bioenergy, but non-edible feedstock resources are significantly under-exploited. Hence it is essential to map the availability of these feedstocks to identify the most appropriate bioenergy solution for each region and develop supply chains for biorefineries. Using Australia as a case study,we present the spatial availability and opportunities for second and third generation feedstocks. Considerations included current land use, the presence of existing biomass industries and climatic conditions. Detailed information on the regional availability of biomass was collected from government statistics, technical reports and energy assessments as well as from academic literature. Second generation biofuels have the largest opportunity in New South Wales, Queensland and Victoria (NSW, QLD and VIC) and the regions with the highest potential for microalgae are Western Australia and Northern Territory (WA, NT), based on land use opportunity cost and climate. The approach can be used in other countries with a similar climate. More research is needed to overcome key technical and economic hurdles.

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The Eating Disorder Risk Composite (EDRC) comprises the Drive for Thinness, Bulimia, and Body Dissatisfaction subscales of the Eating Disorder Inventory, Third Edition (EDI-3, Garner, 2004). Past research conducted with Latina college women (LCW) has found older versions of the EDRC subscales to be reliable, but the EDI-3's EDRC factor structure has yet to be studied among LCW. The present study investigated the pattern of responses to and the factor structure of the EDRC in LCW. It was hypothesized that eating pathology would be present and that a factor analysiswould find some discrepancies between the original factor structure of the EDRC and the factor structure from LCW. Analyses of data on a 6-point Likert scale indicate that drive for thinness and body dissatisfaction are far more prevalent than is bulimic symptomology in LCW. Principal Axis Factoring with promax rotation was used to extract three factors very similar to the original EDRC. Some discrepancies in the item loadings were observed, most notably that half of the items from the original Body Dissatisfaction subscale did not load together on one factor. Overall, the EDRC appears to be a goodmeasurement of eating- and body-related phenomena among LCW. Implications, limitations, and future directions are discussed.

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Aim Simulation forms an increasingly vital component of clinical skills development in a wide range of professional disciplines. Simulation of clinical techniques and equipment is designed to better prepare students for placement by providing an opportunity to learn technical skills in a “safe” academic environment. In radiotherapy training over the last decade or so this has predominantly comprised treatment planning software and small ancillary equipment such as mould room apparatus. Recent virtual reality developments have dramatically changed this approach. Innovative new simulation applications and file processing and interrogation software have helped to fill in the gaps to provide a streamlined virtual workflow solution. This paper outlines the innovations that have enabled this, along with an evaluation of the impact on students and educators. Method Virtual reality software and workflow applications have been developed to enable the following steps of radiation therapy to be simulated in an academic environment: CT scanning using a 3D virtual CT scanner simulation; batch CT duplication; treatment planning; 3D plan evaluation using a virtual linear accelerator; quantitative plan assessment, patient setup with lasers; and image guided radiotherapy software. Results Evaluation of the impact of the virtual reality workflow system highlighted substantial time saving for academic staff as well as positive feedback from students relating to preparation for clinical placements. Students valued practice in the “safe” environment and the opportunity to understand the clinical workflow ahead of clinical department experience. Conclusion Simulation of most of the radiation therapy workflow and tasks is feasible using a raft of virtual reality simulation applications and supporting software. Benefits of this approach include time-saving, embedding of a case-study based approach, increased student confidence, and optimal use of the clinical environment. Ongoing work seeks to determine the impact of simulation on clinical skills.

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Close attention to technical quality or image optimization in transthoracic echocardiography (TTE) is important for the acquisition of high-quality diagnostic images and to ensure that measurements are accurately performed. For this purpose, the echocardiographer must be familiar with all the controls on the ultrasound machine that can be manipulated to optimize the two-dimensional (2D) images, color Doppler images, and spectral Doppler traces...

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Provision of network infrastructure to meet rising network peak demand is increasing the cost of electricity. Addressing this demand is a major imperative for Australian electricity agencies. The network peak demand model reported in this paper provides a quantified decision support tool and a means of understanding the key influences and impacts on network peak demand. An investigation of the system factors impacting residential consumers’ peak demand for electricity was undertaken in Queensland, Australia. Technical factors, such as the customers’ location, housing construction and appliances, were combined with social factors, such as household demographics, culture, trust and knowledge, and Change Management Options (CMOs) such as tariffs, price,managed supply, etc., in a conceptual ‘map’ of the system. A Bayesian network was used to quantify the model and provide insights into the major influential factors and their interactions. The model was also used to examine the reduction in network peak demand with different market-based and government interventions in various customer locations of interest and investigate the relative importance of instituting programs that build trust and knowledge through well designed customer-industry engagement activities. The Bayesian network was implemented via a spreadsheet with a tick box interface. The model combined available data from industry-specific and public sources with relevant expert opinion. The results revealed that the most effective intervention strategies involve combining particular CMOs with associated education and engagement activities. The model demonstrated the importance of designing interventions that take into account the interactions of the various elements of the socio-technical system. The options that provided the greatest impact on peak demand were Off-Peak Tariffs and Managed Supply and increases in the price of electricity. The impact in peak demand reduction differed for each of the locations and highlighted that household numbers, demographics as well as the different climates were significant factors. It presented possible network peak demand reductions which would delay any upgrade of networks, resulting in savings for Queensland utilities and ultimately for households. The use of this systems approach using Bayesian networks to assist the management of peak demand in different modelled locations in Queensland provided insights about the most important elements in the system and the intervention strategies that could be tailored to the targeted customer segments.

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The practice of statistics is the focus of the world in which professional statisticians live. To understand meaningfully what this practice is about, students need to engage in it themselves. Acknowledging the limitations of a genuine classroom setting, this study attempted to expose four classes of year 5 students (n=91) to an authentic experience of the practice of statistics. Setting an overall context of people’s habits that are considered environmentally friendly, the students sampled their class and set criteria for being environmentally friendly based on questions from the Australian Bureau of Statistics CensusAtSchool site. They then analysed the data and made decisions, acknowledging their degree of certainty, about three populations based on their criteria: their class, year 5 students in their school and year 5 students in Australia. The next step was to collect a random sample the size of their class from an Australian Bureau of Statistics ‘population’, analyse it and again make a decision about Australian year 5 students. At the end, they suggested what further research they might do. The analysis of students’ responses gives insight into primary students’ capacity to appreciate and understand decision making, and to participate in the practice of statistics, a topic that has received very little attention in the literature. Based on the total possible score of 23 from student workbook entries, 80 % of students achieved at least a score of 11.

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In the thesis it is discussed in what ways concepts and methodology developed in evolutionary biology can be applied to the explanation and research of language change. The parallel nature of the mechanisms of biological evolution and language change is explored along with the history of the exchange of ideas between these two disciplines. Against this background computational methods developed in evolutionary biology are taken into consideration in terms of their applicability to the study of historical relationships between languages. Different phylogenetic methods are explained in common terminology, avoiding the technical language of statistics. The thesis is on one hand a synthesis of earlier scientific discussion, and on the other an attempt to map out the problems of earlier approaches in addition to finding new guidelines in the study of language change on their basis. Primarily literature about the connections between evolutionary biology and language change, along with research articles describing applications of phylogenetic methods into language change have been used as source material. The thesis starts out by describing the initial development of the disciplines of evolutionary biology and historical linguistics, a process which right from the beginning can be seen to have involved an exchange of ideas concerning the mechanisms of language change and biological evolution. The historical discussion lays the foundation for the handling of the generalised account of selection developed during the recent few decades. This account is aimed for creating a theoretical framework capable of explaining both biological evolution and cultural change as selection processes acting on self-replicating entities. This thesis focusses on the capacity of the generalised account of selection to describe language change as a process of this kind. In biology, the mechanisms of evolution are seen to form populations of genetically related organisms through time. One of the central questions explored in this thesis is whether selection theory makes it possible to picture languages are forming populations of a similar kind, and what a perspective like this can offer to the understanding of language in general. In historical linguistics, the comparative method and other, complementing methods have been traditionally used to study the development of languages from a common ancestral language. Computational, quantitative methods have not become widely used as part of the central methodology of historical linguistics. After the fading of a limited popularity enjoyed by the lexicostatistical method since the 1950s, only in the recent years have also the computational methods of phylogenetic inference used in evolutionary biology been applied to the study of early language history. In this thesis the possibilities offered by the traditional methodology of historical linguistics and the new phylogenetic methods are compared. The methods are approached through the ways in which they have been applied to the Indo-European languages, which is the most thoroughly investigated language family using both the traditional and the phylogenetic methods. The problems of these applications along with the optimal form of the linguistic data used in these methods are explored in the thesis. The mechanisms of biological evolution are seen in the thesis as parallel in a limited sense to the mechanisms of language change, however sufficiently so that the development of a generalised account of selection is deemed as possibly fruiful for understanding language change. These similarities are also seen to support the validity of using phylogenetic methods in the study of language history, although the use of linguistic data and the models of language change employed by these models are seen to await further development.

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Many statistical forecast systems are available to interested users. In order to be useful for decision-making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and their statistical manifestation have been firmly established, the forecasts must also provide some quantitative evidence of `quality’. However, the quality of statistical climate forecast systems (forecast quality) is an ill-defined and frequently misunderstood property. Often, providers and users of such forecast systems are unclear about what ‘quality’ entails and how to measure it, leading to confusion and misinformation. Here we present a generic framework to quantify aspects of forecast quality using an inferential approach to calculate nominal significance levels (p-values) that can be obtained either by directly applying non-parametric statistical tests such as Kruskal-Wallis (KW) or Kolmogorov-Smirnov (KS) or by using Monte-Carlo methods (in the case of forecast skill scores). Once converted to p-values, these forecast quality measures provide a means to objectively evaluate and compare temporal and spatial patterns of forecast quality across datasets and forecast systems. Our analysis demonstrates the importance of providing p-values rather than adopting some arbitrarily chosen significance levels such as p < 0.05 or p < 0.01, which is still common practice. This is illustrated by applying non-parametric tests (such as KW and KS) and skill scoring methods (LEPS and RPSS) to the 5-phase Southern Oscillation Index classification system using historical rainfall data from Australia, The Republic of South Africa and India. The selection of quality measures is solely based on their common use and does not constitute endorsement. We found that non-parametric statistical tests can be adequate proxies for skill measures such as LEPS or RPSS. The framework can be implemented anywhere, regardless of dataset, forecast system or quality measure. Eventually such inferential evidence should be complimented by descriptive statistical methods in order to fully assist in operational risk management.

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Background Surgery is an example of expanded practice scope that enhances podiatry and incorporates inter-professional collaboration. By 2050 demand for foot and ankle procedures is predicted to rise nationally by 61.9%. Performance management of this increase motivated the development of an online audit tool. Developed in collaboration with the Australasian College of Podiatric Surgeons (ACPS), the ACPS audit tool provides real-time data capture and reporting. It is the first audit tool designed in Australia to support and improve the outcomes of foot and ankle surgery. Methods Audit activity in general, orthopaedic, plastic and podiatric surgery was examined using a case study design. Audit participation enablers and barriers were explored. Case study results guided a Delphi survey of international experts experienced or associated with foot and ankle surgery. Delphi survey-derived consensus informed modification of a generic data set from the Royal Australasian College of Surgeons (RACS). Based on the Delphi survey findings the ACPS online audit tool was developed and piloted. Reliability and validity of data entry and usability of this new tool was then assessed with an online survey. Results The case study found surgeon attitudes and behaviours positively impacted audit participation, and also indicated that audit data should be: (1) available in real time, (2) identify practice change, (3) applicable for safety and quality management, and; (4) useful for peer review discussion. The Delphi process established consensus on audit variables to be captured, including the modified RACS generic data set. 382 cases of foot and ankle surgery were captured across 3 months using the new tool. Data entry was found to be valid and reliable. Real-time outcome reporting and practice change identification impacted positively on safety and quality management and assisted peer review discussion. An online survey showed high levels of usability. Conclusions Surgeon contribution to audit tool development resulted in 100% audit participation. The data from the ACPS audit tool supported the ACPS submission to the Medical Services Advisory Committee to list podiatric surgery under Medicare, an outcome noted by the Federal Minister of Health.

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Road traffic accidents are a large problem everywhere in the world. However, regional differences in traffic safety between countries are considerable. For example, traffic safety records are much worse in Southern Europe and the Middle East than in Northern and Western Europe. Despite the large regional differences in traffic safety, factors contributing to different accident risk figures in different countries and regions have remained largely unstudied. The general aim of this study was to investigate regional differences in traffic safety between Southern European/Middle Eastern (i.e., Greece, Iran, Turkey) and Northern/Western European (i.e., Finland, Great Britain, The Netherlands) countries and to identify factors related to these differences. We conducted seven sub-studies in which I applied a traffic culture framework, including a multi-level approach, to traffic safety. We used aggregated level data (national statistics), surveys among drivers, and data on traffic accidents and fatalities in the analyses. In the first study, we investigated the influence of macro level factors (i.e., economic, societal, and cultural) on traffic safety across countries. The results showed that a high GNP per capita and conservatism correlated with a low number of traffic fatalities, whereas a high degree of uncertainty avoidance, neuroticism, and egalitarianism correlated with a high number of traffic fatalities. In the second, third, and fourth studies, we examined whether the conceptualisation of road user characteristics (i.e., driver behaviour and performance) varied across traffic cultures and how these factors determined overall safety, and the differences between countries in traffic safety. The results showed that the factorial agreement for driver behaviour (i.e., aggressive driving) and performance (i.e., safety skills) was unsatisfactory in Greece, Iran, and Turkey, where the lack of social tolerance and interpersonal aggressive violations seem to be important characteristics of driving. In addition, we found that driver behaviour (i.e., aggressive violations and errors) mediated the relationship between culture/country and accidents. Besides, drivers from "dangerous" Southern European countries and Iran scored higher on aggressive violations and errors than did drivers from "safe" Northern European countries. However, "speeding" appeared to be a "pan-cultural" problem in traffic. Similarly, aggressive driving seems largely depend on road users' interactions and drivers' interpretation (i.e., cognitive biases) of the behaviour of others in every country involved in the study. Moreover, in all countries, a risky general driving style was mostly related to being young and male. The results of the fifth and sixth studies showed that among young Turkish drivers, gender stereotypes (i.e., masculinity and femininity) greatly influence driver behaviour and performance. Feminine drivers were safety-oriented whereas masculine drivers were skill-oriented and risky drivers. Since everyday driving tasks involve not only erroneous (i.e., risky or dangerous driving) or correct performance (i.e., normal habitual driving), but also "positive" driver behaviours, we developed a reliable scale for measuring "positive" driver behaviours among Turkish drivers in the seventh study. Consequently, I revised Reason's model [Reason, J. T., 1990. Human error. Cambridge University Press: New York] of aberrant driver behaviour to represent a general driving style, including all possible intentional behaviours in traffic while evaluating the differences between countries in traffic safety. The results emphasise the importance of economic, societal and cultural factors, general driving style and skills, which are related to exposure, cognitive biases as well as age, sex, and gender, in differences between countries in traffic safety.

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Climate variability and change are risk factors for climate sensitive activities such as agriculture. Managing these risks requires "climate knowledge", i.e. a sound understanding of causes and consequences of climate variability and knowledge of potential management options that are suitable in light of the climatic risks posed. Often such information about prognostic variables (e.g. yield, rainfall, run-off) is provided in probabilistic terms (e.g. via cumulative distribution functions, CDF), whereby the quantitative assessments of these alternative management options is based on such CDFs. Sound statistical approaches are needed in order to assess whether difference between such CDFs are intrinsic features of systems dynamics or chance events (i.e. quantifying evidences against an appropriate null hypothesis). Statistical procedures that rely on such a hypothesis testing framework are referred to as "inferential statistics" in contrast to descriptive statistics (e.g. mean, median, variance of population samples, skill scores). Here we report on the extension of some of the existing inferential techniques that provides more relevant and adequate information for decision making under uncertainty.

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The National Health Interview Survey - Disability supplement (NHIS-D) provides information that can be used to understand myriad topics related to health and disability. The survey provides comprehensive information on multiple disability conceptualizations that can be identified using information about health conditions (both physical and mental), activity limitations, and service receipt (e.g. SSI, SSDI, Vocational Rehabilitation). This provides flexibility for researchers in defining populations of interest. This paper provides a description of the data available in the NHIS-D and information on how the data can be used to better understand the lives of people with disabilities.