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Statistical Regression (statistical + regression)
Selected AbstractsContrasting approaches to statistical regression in ecology and economicsJOURNAL OF APPLIED ECOLOGY, Issue 2 2009P. R. Armsworth Summary 1Conservation and natural resource management challenges are as much social problems as biological ones. In recognition of this fact, ecologists and economists work increasingly closely together. We discuss one barrier to effective integration of the two disciplines: put simply, many ecologists and economists approach statistical regression differently. 2Regression techniques provide the most commonly used approach for empirical analyses of land management decisions. Researchers from each discipline attribute differing importance to a range of possibly conflicting design criteria when formulating regression analyses. 3Ecologists commonly attribute greater importance to spatial autocorrelation and parsimony than do economists when designing regressions. Economists often attribute greater importance than ecologists to concerns about endogeneity and conformance with a priori theoretical expectations. 4Synthesis and applications. The differing importance attributed to different design characteristics may reflect a process of cultural drift within each discipline. Greater interdisciplinary collaboration can counteract this process by stimulating the flow of ideas and techniques across disciplinary boundaries. [source] A Review on Residence Time Distribution (RTD) in Food Extruders and Study on the Potential of Neural Networks in RTD ModelingJOURNAL OF FOOD SCIENCE, Issue 6 2002G. Ganjyal ABSTRACT: Residence time distribution and mean residence time depend on process variables, namely feed rate, screw speed, feed moisture content, barrel temperature, die temperature and die diameter. Flow in an extruder has been modeled by simulating residence time distribution, assuming the extruder to be a series of continuous-stirred-tank or plug-flow reactors. Others have developed relationships for mean residence time as functions of process variables. Better models can be developed using neural networks. As an example, data from the literature were used to model mean residence time as a function of process variables using statistical regression and neural networks. Neural network models performed better than regression models. [source] A model of stomatal conductance to quantify the relationship between leaf transpiration, microclimate and soil water stressPLANT CELL & ENVIRONMENT, Issue 11 2002Q. Gao Abstract A model of stomatal conductance was developed to relate plant transpiration rate to photosynthetic active radiation (PAR), vapour pressure deficit and soil water potential. Parameters of the model include sensitivity of osmotic potential of guard cells to photosynthetic active radiation, elastic modulus of guard cell structure, soil-to-leaf conductance and osmotic potential of guard cells at zero PAR. The model was applied to field observations on three functional types that include 11 species in subtropical southern China. Non-linear statistical regression was used to obtain parameters of the model. The result indicated that the model was capable of predicting stomatal conductance of all the 11 species and three functional types under wide ranges of environmental conditions. Major conclusions included that coniferous trees and shrubs were more tolerant for and resistant to soil water stress than broad-leaf trees due to their lower osmotic potential, lignified guard cell walls, and sunken and suspended guard cell structure under subsidiary epidermal cells. Mid-day depression in transpiration and photosynthesis of pines may be explained by decreased stomatal conductance under a large vapour pressure deficit. Stomatal conductance of pine trees was more strongly affected by vapour pressure deficit than that of other species because of their small soil-to-leaf conductance, which is explainable in terms of xylem tracheids in conifer trees. Tracheids transport water by means of small pit-pairs in their side walls, and are much less efficient than the end-perforated vessel members in broad-leaf xylem systems. These conclusions remain hypothetical until direct measurements of these parameters are available. [source] Origins, uses of, and relations between goal programming and data envelopment analysisJOURNAL OF MULTI CRITERIA DECISION ANALYSIS, Issue 1 2005W.W. Cooper Abstract Origins and uses of ,goal programming' and ,data envelopment analysis' (DEA) are identified and discussed. The purpose of this paper is not only to review some of the history of these developments, but also to show some of their uses (e.g. in statistical regression formulations) in order to suggest paths for possible further developments. Turning to how the two types of models relate to each other, the ,additive model' of DEA is shown to have the same structure as a goal programming model in which only ,one-sided deviations' are permitted. A way for formally relating the two to each other is then provided. However, the objectives are differently oriented because goal programming is directed to future performances as part of the planning function whereas DEA is directed to evaluating past performances as part of the control function of management. Other possible ways of comparing and combining the two approaches are also noted including statistical regressions that utilize goal programming to ensure that the resulting estimates satisfy the multi-criteria conditions that are often encountered in managerial applications. Both goal programming and DEA originated in actual applications that were successfully addressed. The research was then generalized and published. This leads to what is referred to as an ,applications-driven theory' strategy for research that is also described in this paper. Copyright © 2006 John Wiley & Sons, Ltd. [source] | ||||||||||