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A Statistical Learning Regression Model Utilized To Determine Predictive Factors Of Social Distancing During Covid-19 Pandemic, Timothy A. Smith, Albert J. Boquet, Matthew V. Chin
A Statistical Learning Regression Model Utilized To Determine Predictive Factors Of Social Distancing During Covid-19 Pandemic, Timothy A. Smith, Albert J. Boquet, Matthew V. Chin
Publications
In an application of the mathematical theory of statistics, predictive regression modelling can be used to determine if there is a trend to predict the response variable of social distancing in terms of multiple predictor input “predictor” variables. In this study the social distancing is measured as the percentage reduction in average mobility by GPS records, and the mathematical results obtained are interpreted to determine what factors drive that response. This study was done on county level data from the state of Florida during the COVID-19 pandemic, and it is found that the most deterministic predictors are county population density …
Nonprofit Leadership: A Study Of Governance Changes Over Time, Daisha M. Merritt
Nonprofit Leadership: A Study Of Governance Changes Over Time, Daisha M. Merritt
Publications
This is a leadership study understood through board governance in nonprofit organizations. The study sought to discover if there were indicators of coercive institutional isomorphism occurring in human service nonprofit organizations in the United States. IRS 990 tax forms were compiled from 2008- 2012 to determine if there were increasing levels of reported governance practices. Methodology included factor analysis, comparison of means, trend analysis, and regression models. Results indicated that there is an overall trend of increasing reported practices of governance in human service nonprofit organizations. Board size is the most significant indicator associated with changes in reported governance practices. …
Winds Of Change?: Projections Of Near-Surface Winds Under Climate Change Scenarios, S C. Pryor, Justin T. Schoof, R J. Barthelmie
Winds Of Change?: Projections Of Near-Surface Winds Under Climate Change Scenarios, S C. Pryor, Justin T. Schoof, R J. Barthelmie
Publications
Changes in near–surface wind speeds due to global climate change may have profound geophysical and societal impacts. However, Global Climate Models (GCMs) are unable to replicate the historically observed magnitude and spatial variability of wind speeds, so we apply a downscaling technique to generate probability distributions of wind speeds at sites in northern Europe for historical periods (1961–1990 and 1982–2000) and two future periods (2046–2065, 2081–2100). Projections for the twenty-first century (C21st) indicate no evidence of substantial evolution relative to the end of the twentieth century (C20th), although there is increased divergence of results from downscaling of different GCMs toward …
Downscaling Temperature And Precipitation: A Comparison Of Regression-Based Methods And Artificial Neural Networks, Justin T. Schoof, S C. Pryor
Downscaling Temperature And Precipitation: A Comparison Of Regression-Based Methods And Artificial Neural Networks, Justin T. Schoof, S C. Pryor
Publications
A comparison of two statistical downscaling methods for daily maximum and minimum surface air temperature, total daily precipitation and total monthly precipitation at Indianapolis, IN, USA, is presented. The analysis is conducted for two seasons, the growing season and the non-growing season, defined based on variability of surface air temperature. The predictors used in the downscaling are indices of the synoptic scale circulation derived from rotated principal components analysis (PCA) and cluster analysis of variables extracted from an 18-year record from seven rawinsonde stations in the Midwest region of the United States. PCA yielded seven significant components for the growing …