A study of the effect of heat source location in a ventilated room using multiple regression analysis

Multiple regression analysis is a statistical technique which allows to predict a dependent variable from m ore than one independent variable and also to determine influential independent variables. Using experimental data, in this study the multiple regression analysis is applied to predict the room mean velocity and determine the most influencing parameters on the velocity. More than 120 experiments for four different heat source locations were carried out in a test chamber with a high level wall mounted air supply terminal at air change rates 3-6 ach. The influence of the environmental parameters such as supply air momentum, room heat load, Archimedes number and local temperature ratio, were examined by two methods: a simple regression analysis incorporated into scatter matrix plots and multiple stepwise regression analysis. It is concluded that, when a heat source is located along the jet centre line, the supply momentum mainly influences the room mean velocity regardless of the plume strength. However, when the heat source is located outside the jet region, the local temperature ratio (the inverse of the local heat removal effectiveness) is a major influencing parameter.

Corporate governance development in privately held family businesses: a multiple case analysis

The objective of this study is to better understand and illustrate the process and the motivations for corporate governance implementation in Brazilian privately held family businesses. Three case companies were analyzed through an adapted developmental framework to illustrate the progression in corporate governance in response to changes in the ownership, investment and management dimensions over time. In this development, causal relationships between corporate governance and the three other framework dimensions were identified. It was found that the analyzed companies´ corporate governance implementation was motivated by the need to curb agency costs, whereas a cornerstone in this development was the first generational change. Only after the family businesses have reached the necessary maturity on all three dimensions, corporate governance practices were implemented. Put simply, the analyzed case companies developed formal systems as they grew more complex. This study complements the academic discussions on corporate governance in family businesses by offering Brazilian evidence on its underlying motivations and sequential implementation over time.

Group analysis method for fission track thermochronology

A methodology to obtain ages and thermal histories of sets of apatite samples from localities with geologically compatible characteristics is described. A methodology exploring the fact that samples with similar geological characteristics should present the same thermal history is proposed. This approach can contribute for the obtainment of more conclusive results by analysing fewer samples than it is necessary when the samples are individually analysed. In order to determine the ages, we use the absolute neutron dosimetry through thin films of natural uranium along with lambda(f) = 8.46 x 10(-17) a(-1). As an example of application of the proposed methodology, we analyse samples collected in a Brazilian region, Sao Francisco Craton, which experienced low tectonic activity. (C) 2008 Elsevier Ltd. All rights reserved.

Monitoring Programmes, Multiple Stress Analysis and Decision Support for River Basin Management

The identification of plausible causes for water body status deterioration will be much easier if it can build on available, reliable, extensive and comprehensive biogeochemical monitoring data (preferably aggregated in a database). A plausible identification of such causes is a prerequisite for well-informed decisions on which mitigation or remediation measures to take. In this chapter, first a rationale for an extended monitoring programme is provided; it is then compared to the one required by the Water Framework Directive (WFD). This proposal includes a list of relevant parameters that are needed for an integrated, a priori status assessment. Secondly, a few sophisticated statistical tools are described that subsequently allow for the estiation of the magnitude of impairment as well as the likely relative importance of different stressors in a multiple stressed environment. The advantages and restrictions of these rather complicated analytical methods are discussed. Finally, the use of Decision Support Systems (DSS) is advocated with regard to the specific WFD implementation requirements.

Wave group analysis based on Kimura's method /

"September 1987."

Program Trixy : multi-group analysis of nuclear reactors in three space dimensions : for the IBM type 704 computer /

"Physics and Math. TID-4500 (15th Ed.)"--Title page.

Use of multiple regression analysis to summarize and interpret linear programming shadow prices in an economic planning model /

Issued May 1980.

Multiple classification analysis ; a report on a computer program for multiple regression using categorical predictors /

Bibliographical footnotes.

Mathematical modelling of patient risks in a hospital environment, using multiple regression analysis

The aim of this research work was primarily to examine the relevance of patient parameters, ward structures, procedures and practices, in respect of the potential hazards of wound cross-infection and nasal colonisation with multiple resistant strains of Staphylococcus aureus, which it is thought might provide a useful indication of a patient's general susceptibility to wound infection. Information from a large cross-sectional survey involving 12,000 patients from some 41 hospitals and 375 wards was collected over a five-year period from 1967-72, and its validity checked before any subsequent analysis was carried out. Many environmental factors and procedures which had previously been thought (but never conclusively proved) to have an influence on wound infection or nasal colonisation rates, were assessed, and subsequently dismissed as not being significant, provided that the standard of the current range of practices and procedures is maintained and not allowed to deteriorate. Retrospective analysis revealed that the probability of wound infection was influenced by the patient's age, duration of pre-operative hospitalisation, sex, type of wound, presence and type of drain, number of patients in ward, and other special risk factors, whilst nasal colonisation was found to be influenced by the patient's age, total duration of hospitalisation, sex, antibiotics, proportion of occupied beds in the ward, average distance between bed centres and special risk factors. A multi-variate regression analysis technique was used to develop statistical models, consisting of variable patient and environmental factors which were found to have a significant influence on the risks pertaining to wound infection and nasal colonisation. A relationship between wound infection and nasal colonisation was then established and this led to the development of a more advanced model for predicting wound infections, taking advantage of the additional knowledge of the patient's state of nasal colonisation prior to operation.

A peak-clustering method for MEG group analysis to minimise artefacts due to smoothness

Magnetoencephalography (MEG), a non-invasive technique for characterizing brain electrical activity, is gaining popularity as a tool for assessing group-level differences between experimental conditions. One method for assessing task-condition effects involves beamforming, where a weighted sum of field measurements is used to tune activity on a voxel-by-voxel basis. However, this method has been shown to produce inhomogeneous smoothness differences as a function of signal-to-noise across a volumetric image, which can then produce false positives at the group level. Here we describe a novel method for group-level analysis with MEG beamformer images that utilizes the peak locations within each participant's volumetric image to assess group-level effects. We compared our peak-clustering algorithm with SnPM using simulated data. We found that our method was immune to artefactual group effects that can arise as a result of inhomogeneous smoothness differences across a volumetric image. We also used our peak-clustering algorithm on experimental data and found that regions were identified that corresponded with task-related regions identified in the literature. These findings suggest that our technique is a robust method for group-level analysis with MEG beamformer images.