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This tutorial teaches you how to use the statistical programming language R to develop a business case simulation and analysis. It presents a methodology for conducting business case analysis that minimizes decision delay by focusing stakeholders on what matters most and suggests pathways for minimizing the risk in strategic and capital allocation decisions. Business case analysis, often conducted in spreadsheets, exposes decision makers to additional risks that arise just from the use of the spreadsheet environment.

The looming antibiotic-resistance crisis has penetrated the consciousness of clinicians, researchers, policymakers, politicians and the public at large. The evolution and widespread distribution of antibiotic-resistance elements in bacterial pathogens has made diseases that were once easily treatable deadly again. Unfortunately, accompanying the rise in global resistance is a failure in antibacterial drug discovery. Lessons from the history of antibiotic discovery and fresh understanding of antibiotic action and the cell biology of microorganisms have the potential to deliver twenty-first century medicines that are able to control infection in the resistance era.

\"How did Americans in the generations following the Declaration of Independence translate its lofty ideals into practice? In this broadly synthetic work, distinguished historian Richard Brown shows that despite its founding statement that \"all men are created equal,\" the early Republic struggled with every form of social inequality. While people paid homage to the ideal of equal rights, this ideal came up against entrenched social and political practices and beliefs. Brown illustrates how the ideal was tested in struggles over race and ethnicity, religious freedom, gender and social class, voting rights and citizenship. He shows how high principles fared in criminal trials and divorce cases when minorities, women, and people from different social classes faced judgment. This book offers a much-needed exploration of the ways revolutionary political ideas penetrated popular thinking and everyday practice\"--Book jacket.

Intracranial saccular or berry aneurysms are common, occurring in about 1–2% of the population. Unruptured intracranial aneurysms are increasingly being detected as cross-sectional imaging techniques are used more frequently in clinical practice. Once an unruptured intracranial aneurysm is detected, decisions regarding optimum management are made on the basis of careful comparison of the short-term and long-term risks of aneurysmal rupture with the risk associated with the intervention, whether that be surgical clipping or endovascular management. Several factors need to be carefully considered, including aneurysm size and location, the patient's family history and medical history, and the availability of an interventional option that has an acceptable risk. The patient's knowledge that they have an unruptured intracranial aneurysm can lead to substantial stress and anxiety, and their perspective regarding treatment, after hearing an unbiased appraisal of the rupture risks and the risk of interventional treatment, is of the utmost importance. Controversy remains regarding optimum management, and thorough assessments of the risks and benefits of contemporary management options, specific to aneurysm size, location, and many other aneurysm and patient factors, are needed.

Markov Chain Monte–Carlo (MCMC) is an increasingly popular method for obtaining information about distributions, especially for estimating posterior distributions in Bayesian inference. This article provides a very basic introduction to MCMC sampling. It describes what MCMC is, and what it can be used for, with simple illustrative examples. Highlighted are some of the benefits and limitations of MCMC sampling, as well as different approaches to circumventing the limitations most likely to trouble cognitive scientists.

Thyroid function affects multiple sites of the female hypothalamic-pituitary gonadal (HPG) axis. Disruption of thyroid function has been linked to reproductive dysfunction in women and is associated with menstrual irregularity, infertility, poor pregnancy outcomes, and gynecological conditions such as premature ovarian insufficiency and polycystic ovarian syndrome. Thus, the complex molecular interplay between hormones involved in thyroid and reproductive functions is further compounded by the association of certain common autoimmune states with disorders of the thyroid and the HPG axes. Furthermore, in prepartum and intrapartum states, even relatively minor disruptions have been shown to adversely impact maternal and fetal outcomes, with some differences of opinion in the management of these conditions. In this review, we provide readers with a foundational understanding of the physiology and pathophysiology of thyroid hormone interactions with the female HPG axis. We also share clinical insights into the management of thyroid dysfunction in reproductive-aged women.

\"This book provides insight into significant and particularly difficult autobiographical memories among vulnerable groups\"-- Provided by publisher.

We examined features of citizen science that influence data quality, inferential power, and usefulness in ecology. As background context for our examination, we considered topics such as ecological sampling (probability based, purposive, opportunistic), linkage between sampling technique and statistical inference (design based, model based), and scientific paradigms (confirmatory, exploratory). We distinguished several types of citizen science investigations, from intensive research with rigorous protocols targeting clearly articulated questions to mass-participation internet-based projects with opportunistic data collection lacking sampling design, and examined overarching objectives, design, analysis, volunteer training, and performance. We identified key features that influence data quality: project objectives, design and analysis, and volunteer training and performance. Projects with good designs, trained volunteers, and professional oversight can meet statistical criteria to produce high-quality data with strong inferential power and therefore are well suited for ecological research objectives. Projects with opportunistic data collection, little or no sampling design, and minimal volunteer training are better suited for general objectives related to public education or data exploration because reliable statistical estimation can be difficult or impossible. In some cases, statistically robust analytical methods, external data, or both may increase the inferential power of certain opportunistically collected data. Ecological management, especially by government agencies, frequently requires data suitable for reliable inference. With standardized protocols, state-of-the-art analytical methods, and well-supervised programs, citizen science can make valuable contributions to conservation by increasing the scope of species monitoring efforts. Data quality can be improved by adhering to basic principles of data collection and analysis, designing studies to provide the data quality required, and including suitable statistical expertise, thereby strengthening the science aspect of citizen science and enhancing acceptance by the scientific community and decision makers. Examinamos las características de la ciencia ciudadana que influyen sobre la calidad de datos, el poder inferencial, y la utilidad en la ecología. Consideramos temas como el muestreo ecológico (basado en probabilidad, deliberado, oportunista), la conexión entre la técnica de muestreo y la inferencia estadística (basada en diseño, basada en modelo) y los paradigmas científicos (confirmatorio, exploratorio) como trasfondo contextual para nuestra evaluación. Distinguimos varios tipos de investigación de ciencia ciudadana, desde investigación intensiva con protocolos rigurosos enfocados en preguntas claramente articuladas hasta proyectos de participación masiva en plataformas de internet con recolección de datos oportunistas carentes de un diseño de muestreo, y examinamos los objetivos generales, el diseño, el análisis, y la preparación de los voluntarios y el desempeño. Identificamos características clave que influyen sobre la calidad de los datos: los objetivos del proyecto, el diseño y el análisis, y la preparación y el desempeño de los voluntarios. Los proyectos con buenos diseños, voluntarios preparados, y supervisión profesional pueden cumplir con criterios estadísticos para producir datos de alta calidad con un fuerte poder inferencial, y por lo tanto son muy adecuados para los objetivos de investigación ecológica. Los proyectos con una recolección oportunista de datos, un diseño de muestreo ínfimo o nulo, y una preparación mínima de los voluntarios son más adecuados para los objetivos generales relacionados con la educación pública o la exploración de datos ya que la estimación estadística confiable puede ser complicada o imposible. En algunos casos los métodos analíticos estadísticamente sólidos, los datos externos, o ambos, pueden incrementar el poder inferencial de ciertos datos recolectados de manera oportunista. El manejo ecológico, en especial el que realizan las agencias gubernamentales, requiere frecuentemente de datos apropiados para una inferencia confiable. Con protocolos estandarizados, métodos analíticos modernos, y programas supervisados correctamente, la ciencia ciudadana puede contribuir de forma valiosa a la conservación al incrementar el alcance de los esfuerzos de monitoreo para una especie. La calidad de datos puede mejorarse si se adhiere a los principios básicos de la recolección y análisis de datos, se diseñan los estudios para que proporcionen la calidad requerida de datos, y si se incluye una pericia estadística adecuada, fortaleciendo así el aspecto científico de la ciencia ciudadana y aumentando su aceptación dentro de la comunidad científica y con quienes toman las decisiones. 本研究分析了生态学中影响数据质量.、推论统计效カ和有用性的公民科学的特征。检验的背景包括如生 态学抽样(基于概率的抽样、目的抽样、机会抽样X 抽样技术与统计推断(基于设计或基于模型) 的联系，以及 科学范式(验怔性或探索性) 等话题。我们区分出不同类型的公民科学调查，从有清晰明确的问题及严格实验规 范的深入研究，到缺少抽样设计、投机型数据收集的基于互联网的大规模参与项目；并研究了项目的总体目标、 设计、分析、志愿者培训和实现情况。本研究确定了影响数据质量的关键特征，包括项目目标、设计和分析、 志愿者培训和实现情況。拥有良好的设计、训练有素的志愿者和专业监督的项目通常符合统计学标准，能获得 推论统计效カ强的高质量数据，因此可以达到生态学研究目标。而投机型数据收集、很少或没有进行抽样设计 且志愿者培训非常有限的项目，更适合与公共教育或数据探索相关的一般性目标，因为它们很难或不可能进行可 靠的统计估计。在一些情况下，统计上強健的分析方法和 外部数据也会増加某些投机型数据的推论效力。 生态管理特别是来自政府机构的管理，常常需要那些适合进行可靠统计推论的数据。当采用标准化的实验规 范、最先进的分析方法和良好监督的程序时，公民科学可以扩大物种监测范围，为保护做出重要贡献。坚持数据 收集和分析的基本原则、设计研究方案来提供所需的高质量数据，并恰当运用统计学知识，能够增强数据质置 从而加强公民科学的科学性，提高科学界和决策者对公民科学的接受度。