Regression Model

Thailand experiences severe floods and droughts that affect agriculture New techniques, such as Data-Based- Mechanistic modelling ale being developed to study rainfall and river flow to improve flood and drought alleviation policies and practices. Dynamic Harmonic Regression models are used to analyze rainfall and discharge time series across Thailand to define seasonality, trends and to forecast rainfall and discharge and their spatial distribution. Statistical patterns in the frequency of extreme rainfall and flow periods are identified with a view to improving predictions of medium and Longer-Term rainfall and river flow patterns. The results show temporal and spatial variation within the annual rainfall pattern in the study catchments. For example, the seasonality of the rainfall In the south is less pronounced (more equatorial). The discharge seasonal pattern shows stronger Semi-Annual cycles, with the weakest pattern in the south of country, whereas the strongest discharge sea...

I demonstrate the application of hierarchical regression modeling, a state-of-the-art technique for statistical inference, to language research. First, a stable sociolinguistic variable in Philadelphia (Labov, 2001) is reconsidered, with attention paid to the treatment of collinearities among socioeconomic predictors. I then demonstrate the use of hierarchical models to account for the random sampling of subjects and items in an experimental setting, using data from a study of word-learning in the face of tonal variation (Quam and Swingley ...

For centuries, land degradation triggered by deforestation has occurred in Ethiopia, in particular in the northern regional state Tigray, the area under study. In order to change this situation, the local government started to establish enclosures. In these sites, grazing is no longer permitted so that forest can naturally regenerate. In order to develop sustainable yield planning for forest rehabilitation

Previous research has shown that pharmacoeconomic (PE) data are considered important but may not be optimally utilized by decision makers. No research has compared the effectiveness of different types of PE models. The purpose of this study was to examine the perceived value and understanding of PE models among decision makers in managed care organizations. The perspective of this study was from research scientists working in the pharmaceutical industry who present PE models to managed care clients. The study objectives were to (1) examine what types of models are best received by decision makers, (2) investigate the barriers to using PE models, and (3) recommend methods for improving PE models. A telephone survey of 39 items was conducted with 20 PE research scientists from various U.S. pharmaceutical and biotechnology companies. Topics addressed included factors contributing to how well PE models are received, barriers to using PE models, and recommendations for improving PE model...

Regression analysis is intended to be used when the researcher seeks to test a given hypothesis against a data set. Unfortunately, in many applications it is either not possible to specify a hypothesis, typically because the research is in a very early stage, or it is not desirable to form a hypothesis, typically because the number of potential explanatory variables is very large. In these cases, researchers have resorted either to overt data mining techniques such as stepwise regression, or covert data mining techniques such as running variations on regression models prior to running the final model (also known as “data peeking”). While data mining side-steps the need to form a hypothesis, it is highly susceptible to generating spurious results. This paper draws on the known properties of OLS estimators in the presence of omitted and extraneous variable models to propose a procedure for data mining that attempts to distinguish between parameter estimates that are significant due to...