992 resultados para Statistical index


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Recent data indicate that levels of overweight and obesity are increasing at an alarming rate throughout the world. At a population level (and commonly to assess individual health risk), the prevalence of overweight and obesity is calculated using cut-offs of the Body Mass Index (BMI) derived from height and weight. Similarly, the BMI is also used to classify individuals and to provide a notional indication of potential health risk. It is likely that epidemiologic surveys that are reliant on BMI as a measure of adiposity will overestimate the number of individuals in the overweight (and slightly obese) categories. This tendency to misclassify individuals may be more pronounced in athletic populations or groups in which the proportion of more active individuals is higher. This differential is most pronounced in sports where it is advantageous to have a high BMI (but not necessarily high fatness). To illustrate this point we calculated the BMIs of international professional rugby players from the four teams involved in the semi-finals of the 2003 Rugby Union World Cup. According to the World Health Organisation (WHO) cut-offs for BMI, approximately 65% of the players were classified as overweight and approximately 25% as obese. These findings demonstrate that a high BMI is commonplace (and a potentially desirable attribute for sport performance) in professional rugby players. An unanswered question is what proportion of the wider population, classified as overweight (or obese) according to the BMI, is misclassified according to both fatness and health risk? It is evident that being overweight should not be an obstacle to a physically active lifestyle. Similarly, a reliance on BMI alone may misclassify a number of individuals who might otherwise have been automatically considered fat and/or unfit.

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Background The problem of silent multiple comparisons is one of the most difficult statistical problems faced by scientists. It is a particular problem for investigating a one-off cancer cluster reported to a health department because any one of hundreds, or possibly thousands, of neighbourhoods, schools, or workplaces could have reported a cluster, which could have been for any one of several types of cancer or any one of several time periods. Methods This paper contrasts the frequentist approach with a Bayesian approach for dealing with silent multiple comparisons in the context of a one-off cluster reported to a health department. Two published cluster investigations were re-analysed using the Dunn-Sidak method to adjust frequentist p-values and confidence intervals for silent multiple comparisons. Bayesian methods were based on the Gamma distribution. Results Bayesian analysis with non-informative priors produced results similar to the frequentist analysis, and suggested that both clusters represented a statistical excess. In the frequentist framework, the statistical significance of both clusters was extremely sensitive to the number of silent multiple comparisons, which can only ever be a subjective "guesstimate". The Bayesian approach is also subjective: whether there is an apparent statistical excess depends on the specified prior. Conclusion In cluster investigations, the frequentist approach is just as subjective as the Bayesian approach, but the Bayesian approach is less ambitious in that it treats the analysis as a synthesis of data and personal judgements (possibly poor ones), rather than objective reality. Bayesian analysis is (arguably) a useful tool to support complicated decision-making, because it makes the uncertainty associated with silent multiple comparisons explicit.

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In this study we incorporate a pre-internationalisation phase into the traditional Uppsala model of firm internationalisation to address the question: Where does the process begin? We identify through the literature four concepts fundamental to the ability of a firm to begin internationalisation of its operations: stimuli, attitudinal/psychological commitment, resources and lateral rigidity. Through a survey of 274 Australian exporting and non-exporting firms we collect data relating to the four pre-internationalisation concepts. An internationalisation readiness index is constructed and applied to some representative cases to establish its validity as a diagnostic tool.

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Agricultural production is one of the major industries in New Zealand and accounts for over 60% of all export trade. The farming industry comprises 70,000 entities ranging in size from small individual run farms to large corporate operations. The reliance of the New Zealand economy to the international rural sector has seen considerable volatility in the rural land markets over the past four decades, with significant shifts in rural land prices based on location, land use and underlying international rural commodity prices. With the increasing attention being paid to the rural sector, especially in relation to food production and bio-fuels, there has been an increasing corporate interest in rural land ownership in relatively low subsidised agricultural producing countries such as New Zealand and Australia. A factor that has limited this participation of institutional investors previously has been a lack of reliable and up-to-date investment performance data for this asset class. This paper is the initial starting phase in the development of a New Zealand South Island rural land investment performance index and covers the period 1990-2007. The research in this paper analyses all rural sales transactions in the South Island and develops a capital return index for rural property based on major rural property land use. Additional work on this index will cover both total return performance and geographic location.