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Some of these they obtain from remote sensing, such as the area of contiguous grassland embedding each field proximity to trees and the percentages of tree cover, grassland cover, wetland cover, and cropland cover within 400 m, 800 m, and 1,200 m of a field. They also record a number of potential explanatory variables. They select, as randomly as feasible, a number of fields to study and record the number of each species present in each field during the birds’ breeding season. Their objective is to identify features conducive to supporting high densities of grassland birds. Because of the concern about declining populations of many grassland birds, investigators decide to study a number of grassland fields in the Midwest. Government work and is in the public domain in the USA.Ĭonsider the following-very realistic-scenario. Understanding the causal mechanism will provide much better predictions beyond the range of data observed.
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Determining causation is far more valuable than simply indicating how the response variable and explanatory variables covaried within a data set, especially when the data set did not arise from a controlled experiment. Further, we argue that, although multimodel inference can be useful in natural resource management, the importance of considering causality and accurately estimating effect sizes is greater than simply considering a variety of models. Although there are advantages and disadvantages to each approach, we suggest that there is no unique best way to analyze data.
#DR.WEB CUREIT 2015 FULL#
We consider a variety of regression modeling strategies for analyzing observational data associated with typical wildlife studies, including all subsets and stepwise regression, a single full model, and Akaike's Information Criterion (AIC)-based multimodel inference.