Problem formulation and organizational decision-making : biases and assumptions underlying alternative models of strategic problem formulation

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The CDA Foundation model to fluoridate communities.

California's population receiving the benefits of fluoridated public water supplies has increased from 15.7 percent to 62.1 percent in the past 20 years. This growth has been achieved through a broad-based coalition of organizations and individuals, starting with the creation of the California Fluoridation Task Force in 1994 and supported by the California Fluoridation Act of 1995. This paper describes the process whereby the most recent gains have been made in San Diego and are ongoing in San Jose

Inside Hollins (1940)

https://digitalcommons.hollins.edu/insideh/1000/thumbnail.jp

Comparison of the Performance of Simple Linear Regression and Quantile Regression with Non-Normal Data: A Simulation Study

Linear regression is a widely used method for analysis that is well understood across a wide variety of disciplines. In order to use linear regression, a number of assumptions must be met. These assumptions, specifically normality and homoscedasticity of the error distribution can at best be met only approximately with real data. Quantile regression requires fewer assumptions, which offers a potential advantage over linear regression. In this simulation study, we compare the performance of linear (least squares) regression to quantile regression when these assumptions are violated, in order to investigate under what conditions quantile regression becomes the more advantageous method of analysis. Statistical power and coverage percentage were calculated for all simulations, and potential bias was investigated for both quantile regression and linear regression. When errors are skewed, there is a threshold at which quantile regression surpasses linear regression in statistical power. When heteroscedasticity is introduced, linear regression does not accurately describe the relationship between predictor and response variables at the tails of the conditional distribution. When errors are both skewed and heteroscedastic, quantile regression performs drastically better than linear regression. Coverage percentage in linear regression not only suffers in this case, but also linear regression yields misleading results

Digital Repository of Mathematical Formulae

The purpose of the NIST Digital Repository of Mathematical Formulae (DRMF) is to create a digital compendium of mathematical formulae for orthogonal polynomials and special functions (OPSF) and of associated mathematical data. The DRMF addresses needs of working mathematicians, physicists and engineers: providing a platform for publication and interaction with OPSF formulae on the web. Using MediaWiki extensions and other existing technology (such as software and macro collections developed for the NIST Digital Library of Mathematical Functions), the DRMF acts as an interactive web domain for OPSF formulae. Whereas Wikipedia and other web authoring tools manifest notions or descriptions as first class objects, the DRMF does that with mathematical formulae. See http://gw32.iu.xsede.org/index.php/Main_Page

Inside Hollins (1942)

https://digitalcommons.hollins.edu/insideh/1002/thumbnail.jp

Inside Hollins (1947)

https://digitalcommons.hollins.edu/insideh/1003/thumbnail.jp