BEST SUBSET SELECTION FOR CATEGORICAL DATA

When choosing among models to describe categorical data, the necessity to consider interactions makes selection more difficult. With just four variables, considering all interactions, there are 166 different hierarchical models and many more non-hierarchical models. Two procedures have been developed for categorical data which will produce the "best" subset or subsets of each model size where size refers to the number of effects in the model. Both procedures are patterned after the Leaps and Bounds approach used by Furnival and Wilson for continuous data and do not generally require fitting all models. For hierarchical models, likelihood ratio statistics (G('2)) are computed using iterative proportional fitting and "best" is determined by comparing, among models with the same number of effects, the Pr((chi)(,k)('2) (GREATERTHEQ) G(,ij)('2)) where k is the degrees of freedom for ith model of size j. To fit non-hierarchical as well as hierarchical models, a weighted least squares procedure has been developed.^ The procedures are applied to published occupational data relating to the occurrence of byssinosis. These results are compared to previously published analyses of the same data. Also, the procedures are applied to published data on symptoms in psychiatric patients and again compared to previously published analyses.^ These procedures will make categorical data analysis more accessible to researchers who are not statisticians. The procedures should also encourage more complex exploratory analyses of epidemiologic data and contribute to the development of new hypotheses for study. ^

New methods for quantification and analysis of quantitative real-time polymerase chain reaction data

Quantitative real-time polymerase chain reaction (qPCR) is a sensitive gene quantitation method that has been widely used in the biological and biomedical fields. The currently used methods for PCR data analysis, including the threshold cycle (CT) method, linear and non-linear model fitting methods, all require subtracting background fluorescence. However, the removal of background fluorescence is usually inaccurate, and therefore can distort results. Here, we propose a new method, the taking-difference linear regression method, to overcome this limitation. Briefly, for each two consecutive PCR cycles, we subtracted the fluorescence in the former cycle from that in the later cycle, transforming the n cycle raw data into n-1 cycle data. Then linear regression was applied to the natural logarithm of the transformed data. Finally, amplification efficiencies and the initial DNA molecular numbers were calculated for each PCR run. To evaluate this new method, we compared it in terms of accuracy and precision with the original linear regression method with three background corrections, being the mean of cycles 1-3, the mean of cycles 3-7, and the minimum. Three criteria, including threshold identification, max R2, and max slope, were employed to search for target data points. Considering that PCR data are time series data, we also applied linear mixed models. Collectively, when the threshold identification criterion was applied and when the linear mixed model was adopted, the taking-difference linear regression method was superior as it gave an accurate estimation of initial DNA amount and a reasonable estimation of PCR amplification efficiencies. When the criteria of max R2 and max slope were used, the original linear regression method gave an accurate estimation of initial DNA amount. Overall, the taking-difference linear regression method avoids the error in subtracting an unknown background and thus it is theoretically more accurate and reliable. This method is easy to perform and the taking-difference strategy can be extended to all current methods for qPCR data analysis.^

Reference list of 120 datasets from time series station Payerne used for exploratory search

The analysis of time-dependent data is an important problem in many application domains, and interactive visualization of time-series data can help in understanding patterns in large time series data. Many effective approaches already exist for visual analysis of univariate time series supporting tasks such as assessment of data quality, detection of outliers, or identification of periodically or frequently occurring patterns. However, much fewer approaches exist which support multivariate time series. The existence of multiple values per time stamp makes the analysis task per se harder, and existing visualization techniques often do not scale well. We introduce an approach for visual analysis of large multivariate time-dependent data, based on the idea of projecting multivariate measurements to a 2D display, visualizing the time dimension by trajectories. We use visual data aggregation metaphors based on grouping of similar data elements to scale with multivariate time series. Aggregation procedures can either be based on statistical properties of the data or on data clustering routines. Appropriately defined user controls allow to navigate and explore the data and interactively steer the parameters of the data aggregation to enhance data analysis. We present an implementation of our approach and apply it on a comprehensive data set from the field of earth bservation, demonstrating the applicability and usefulness of our approach.

Guide to reporting highway statistics: procedures for the compilation, analysis and reporting of: state motor-fuel, motor-vehicle, driver-license and motor-carrier data; highway finance data of state and local governments.

Mode of access: Internet.

Pedestrian accidents occurring on freeways: an investigation of causative factors, accident data collection and analysis. Volume III: appendixes. Final report, phases III and IV.

Federal Highway Administration, Washington, D.C.

Pedestrian accidents occurring on freeways: an investigation of causative factors, accident data collection and analysis. Volume I: executive summary. Final report, phases III and IV.

Federal Highway Administration, Washington, D.C.

Pedestrian accidents occurring on freeways: an investigation of causative factors, accident data collection and analysis. Volume II. Final report, phases III and IV.

Federal Highway Administration, Washington, D.C.

Causative factors and countermeasures for rural and suburban pedestrian accidents: accident data collection and analysis - appendices. Phase I and II.

Federal Highway Administration, Washington, D.C.

The use and analysis of recorded traffic accident data. Final report.

Texas State Department of Highways and Public Transportation, Transportation Planning Division, Austin

An introduction to analysis of variance (ANOVA) with special reference to data from clinical experiments in optometry

This article is aimed primarily at eye care practitioners who are undertaking advanced clinical research, and who wish to apply analysis of variance (ANOVA) to their data. ANOVA is a data analysis method of great utility and flexibility. This article describes why and how ANOVA was developed, the basic logic which underlies the method and the assumptions that the method makes for it to be validly applied to data from clinical experiments in optometry. The application of the method to the analysis of a simple data set is then described. In addition, the methods available for making planned comparisons between treatment means and for making post hoc tests are evaluated. The problem of determining the number of replicates or patients required in a given experimental situation is also discussed. Copyright (C) 2000 The College of Optometrists.

A configuration framework of manufacturing strategy paradigm in state owned enterprises within China's petrochemical industry:building context-embedded configurations for the paradigm of strategic manufacturing management from exploratory case studies

The role of the production system as a key determinant of competitive performance of business operations- has long been the subject of industrial organization research, even predating the .explicit conceptua1isation of manufacturing, strategy in the literature. Particular emergent production issues such as the globalisation of production, global supply chain management, management of integrated manufacturing and a growing e~busjness environment are expected to critically influence the overall competitive performance and therefore the strategic success of the organization. More than ever, there is a critical need to configure and improve production system and operations competence in a strategic way so as to contribute to the long-term competitiveness of the organization. In order to operate competitively and profitably, manufacturing companies, no matter how well managed, all need a long-term 'strategic direction' for the development of operations competence in order to consistently produce more market value with less cost towards a leadership position. As to the long-term competitiveness, it is more important to establish a dynamic 'strategic perspective' for continuous operational improvements in pursuit of this direction, as well as ongoing reviews of the direction in relation to the overall operating context. However, it also clear that the 'existing paradigm of manufacturing strategy development' is incapable of adequately responding to the increasing complexities and variations of contemporary business operations. This has been factually reflected as many manufacturing companies are finding that methodologies advocated in the existing paradigm for developing manufacturing strategy have very limited scale and scope for contextual contingency in empirical application. More importantly, there has also emerged a deficiency in the multidimensional and integrative profile from a theoretical perspective when operationalising the underlying concept of strategic manufacturing management established in the literature. The point of departure for this study was a recognition of such contextual and unitary limitations in the existing paradigm of manufacturing strategy development when applied to contemporary industrial organizations in general, and Chinese State Owned Enterprises (SOEs) in particular. As China gradually becomes integrated into the world economy, the relevance of Western management theory and its paradigm becomes a practical matter as much as a theoretical issue. Since China markedly differs from Western countries in terms of culture, society, and political and economic systems, it presents promising grounds to test and refine existing management theories and paradigms with greater contextual contingency and wider theoretical perspective. Under China's ongoing programmes of SOE reform, there has been an increased recognition that strategy development is the very essence of the management task for managers of manufacturing companies in the same way as it is for their counterparts in Western economies. However, the Western paradigm often displays a rather naive and unitary perspective of the nature of strategic management decision-making, one which largely overlooks context-embedded factors and social/political influences on the development of manufacturing strategy. This thesis studies the successful experiences of developing manufacturing strategy from five high-performing large-scale SOEs within China’s petrochemical industry. China’s petrochemical industry constitutes a basic heavy industrial sector, which has always been a strategic focus for reform and development by the Chinese government. Using a confirmation approach, the study has focused on exploring and conceptualising the empirical paradigm of manufacturing strategy development practiced by management. That is examining the ‘empirical specifics’ and surfacing the ‘managerial perceptions’ of content configuration, context of consideration, and process organization for developing a manufacturing strategy during the practice. The research investigation adopts a qualitative exploratory case study methodology with a semi-structural front-end research design. Data collection follows a longitudinal and multiple-case design and triangulates case evidence from sources including qualitative interviews, direct observation, and a search of documentations and archival records. Data analysis follows an investigative progression from a within-case preliminary interpretation of facts to a cross-case search for patterns through theoretical comparison and analytical generalization. The underlying conceptions in both the literature of manufacturing strategy and related studies in business strategy were used to develop theoretical framework and analytical templates applied during data collection and analysis. The thesis makes both empirical and theoretical contributions to our understanding of 'contemporary management paradigm of manufacturing strategy development'. First, it provides a valuable contextual contingency of the 'subject' using the business setting of China's SOEs in petrochemical industry. This has been unpacked into empirical configurations developed for its context of consideration, its content and process respectively. Of special note, a lean paradigm of business operations and production management discovered at case companies has significant implications as an emerging alternative for high-volume capital intensive state manufacturing in China. Second, it provides a multidimensional and integrative theoretical profile of the 'subject' based upon managerial perspectives conceptualised at case companies when operationalising manufacturing strategy. This has been unpacked into conceptual frameworks developed for its context of consideration, its content constructs, and its process patterns respectively. Notably, a synergies perspective towards the operating context, competitive priorities and competence development of business operations and production management has significant implications for implementing a lean manufacturing paradigm. As a whole, in so doing, the thesis established a theoretical platform for future refinement and development of context-specific methodologies for developing manufacturing strategy.

Quantitative neuropathology:data collection and statistical analysis

The use of quantitative methods has become increasingly important in the study of neuropathology and especially in neurodegenerative disease. Disorders such as Alzheimer's disease (AD) and the frontotemporal dementias (FTD) are characterized by the formation of discrete, microscopic, pathological lesions which play an important role in pathological diagnosis. This chapter reviews the advantages and limitations of the different methods of quantifying pathological lesions in histological sections including estimates of density, frequency, coverage, and the use of semi-quantitative scores. The sampling strategies by which these quantitative measures can be obtained from histological sections, including plot or quadrat sampling, transect sampling, and point-quarter sampling, are described. In addition, data analysis methods commonly used to analysis quantitative data in neuropathology, including analysis of variance (ANOVA), polynomial curve fitting, multiple regression, classification trees, and principal components analysis (PCA), are discussed. These methods are illustrated with reference to quantitative studies of a variety of neurodegenerative disorders.

Analysis and Data Mining of Lead-Zinc Ore Data

This paper presents the results of our data mining study of Pb-Zn (lead-zinc) ore assay records from a mine enterprise in Bulgaria. We examined the dataset, cleaned outliers, visualized the data, and created dataset statistics. A Pb-Zn cluster data mining model was created for segmentation and prediction of Pb-Zn ore assay data. The Pb-Zn cluster data model consists of five clusters and DMX queries. We analyzed the Pb-Zn cluster content, size, structure, and characteristics. The set of the DMX queries allows for browsing and managing the clusters, as well as predicting ore assay records. A testing and validation of the Pb-Zn cluster data mining model was developed in order to show its reasonable accuracy before beingused in a production environment. The Pb-Zn cluster data mining model can be used for changes of the mine grinding and floatation processing parameters in almost real-time, which is important for the efficiency of the Pb-Zn ore beneficiation process. ACM Computing Classification System (1998): H.2.8, H.3.3.

Bagging model with cost sensitive analysis on diabetes data

Diabetes patients might suffer from an unhealthy life, long-term treatment and chronic complicated diseases. The decreasing hospitalization rate is a crucial problem for health care centers. This study combines the bagging method with base classifier decision tree and costs-sensitive analysis for diabetes patients' classification purpose. Real patients' data collected from a regional hospital in Thailand were analyzed. The relevance factors were selected and used to construct base classifier decision tree models to classify diabetes and non-diabetes patients. The bagging method was then applied to improve accuracy. Finally, asymmetric classification cost matrices were used to give more alternative models for diabetes data analysis.

Options to advance co-ordination and analysis of climate data in Europe

Systematic, high-quality observations of the atmosphere, oceans and terrestrial environments are required to improve understanding of climate characteristics and the consequences of climate change. The overall aim of this report is to carry out a comparative assessment of approaches taken to addressing the state of European observations systems and related data analysis by some leading actors in the field. This research reports on approaches to climate observations and analyses in Ireland, Switzerland, Germany, The Netherlands and Austria and explores options for a more coordinated approach to national responses to climate observations in Europe. The key aspects addressed are: an assessment of approaches to develop GCOS and provision of analysis of GCOS data; an evaluation of how these countries are reporting development of GCOS; highlighting best practice in advancing GCOS implementation including analysis of Essential Climate Variables (ECVs); a comparative summary of the differences and synergies in terms of the reporting of climate observations; an overview of relevant European initiatives and recommendations on how identified gaps might be addressed in the short to medium term.