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In CLASSICAL AND MODERN REGRESSION WITH APPLICATIONS, Second Edition, Raymond H. The results provide valuable insights into methods for landscape-scale mapping of cavity trees for wildlife habitat management, and also on sample size determination for cavity tree surveys and monitoring. Regression analysis is a vitally important statistical tool, with major advancements made by both practical data analysts and statistical theorists. We successfully explored the utility of three classes of models for predicting cavity tree probability/density: logistic regression, neural network, and classification and regression tree (CART). We illustrate the prediction error monotonically decreases as the spatial scale of predictions in-creases. However, we further show that it is possible to model and predict mean cavity tree density for larger spatial areas.
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We utilized data from the Missouri Ozark Forest Ecosystem Project to show that it is virtually impossible to accurately predict cavity tree occurrence for individual trees or to predict mean cavity tree abundance for individual forest stands. This makes it difficult to model and predict cavity tree density. However, cavity trees are relatively rare and their abundance can vary dramatically among forest stands, even when the stands are similar in most other respects. Thus, monitoring and predicting cavity tree abundance is an important part of forest management and wildlife conservation. Boston: PWS Publishing Company.Įffect of Spatial Scale on Modeling and Predicting Mean Cavity Tree Density: A Comparison of Modeling MethodsĬART, Logistic Regression Neural Network Oak Forest Prediction AccuracyĪBSTRACT: Cavity trees are integral components of healthy forest ecosystems and provide habitat and shelter for a wide variety of wildlife species.
CLASSICAL AND MODERN REGRESSION WITH APPLICATIONS PDF DOWNLOAD WINDOWS 7
Gmail desktop application for windows 7 free download. Classical and Modern Regression with Applications. Keywords: latent class analysis, latent class regression, polytomous, categorical, concomitant.