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Statism and the Economy: The Deadliest Virus
by Jesús Huerta de SotoFeaturing essays on topics ranging from the pandemic to antideflationist paranoia and the crisis of classical liberalism, this volume explores the various ways in which socialism statism is the ‘deadliest virus’ which constantly endangers the spontaneous process of social cooperation. Drawing on the Austrian School of economics, the book includes writings on the monetary policy of the European Central Bank and the economics of pandemics, economic cycles, Japanization and deflation, the crisis of classical liberalism versus anarchocapitalism, market socialism and nationalism, and the relations between efficiency and ethics. This book will be of great interest to those engaged with the study of Austrian economics, economic methodology, the monetary policy of the European Central Bank, the economic theory of pandemics, the theory of banking and economic cycles, the theory of dynamic efficiency and the history of economic thought.
Statism and the Economy: The Deadliest Virus
by Jesús Huerta de SotoFeaturing essays on topics ranging from the pandemic to antideflationist paranoia and the crisis of classical liberalism, this volume explores the various ways in which socialism statism is the ‘deadliest virus’ which constantly endangers the spontaneous process of social cooperation. Drawing on the Austrian School of economics, the book includes writings on the monetary policy of the European Central Bank and the economics of pandemics, economic cycles, Japanization and deflation, the crisis of classical liberalism versus anarchocapitalism, market socialism and nationalism, and the relations between efficiency and ethics. This book will be of great interest to those engaged with the study of Austrian economics, economic methodology, the monetary policy of the European Central Bank, the economic theory of pandemics, the theory of banking and economic cycles, the theory of dynamic efficiency and the history of economic thought.
Statistical Analysis for Decision Makers in Healthcare: Understanding and Evaluating Critical Information in Changing Times
by Jeffrey C. BauerAmericans are bombarded with statistical data each and every day, and healthcare professionals are no exception. All segments of healthcare rely on data provided by insurance companies, consultants, research firms, and the federal government to help them make a host of decisions regarding the delivery of medical services. But while these health pro
Statistical Analysis of Financial Data: With Examples In R (Chapman & Hall/CRC Texts in Statistical Science)
by James GentleStatistical Analysis of Financial Data covers the use of statistical analysis and the methods of data science to model and analyze financial data. The first chapter is an overview of financial markets, describing the market operations and using exploratory data analysis to illustrate the nature of financial data. The software used to obtain the data for the examples in the first chapter and for all computations and to produce the graphs is R. However discussion of R is deferred to an appendix to the first chapter, where the basics of R, especially those most relevant in financial applications, are presented and illustrated. The appendix also describes how to use R to obtain current financial data from the internet. Chapter 2 describes the methods of exploratory data analysis, especially graphical methods, and illustrates them on real financial data. Chapter 3 covers probability distributions useful in financial analysis, especially heavy-tailed distributions, and describes methods of computer simulation of financial data. Chapter 4 covers basic methods of statistical inference, especially the use of linear models in analysis, and Chapter 5 describes methods of time series with special emphasis on models and methods applicable to analysis of financial data. Features * Covers statistical methods for analyzing models appropriate for financial data, especially models with outliers or heavy-tailed distributions. * Describes both the basics of R and advanced techniques useful in financial data analysis. * Driven by real, current financial data, not just stale data deposited on some static website. * Includes a large number of exercises, many requiring the use of open-source software to acquire real financial data from the internet and to analyze it.
Statistical Analysis of Management Data
by Hubert GatignonStatistical Analysis of Management Data provides a comprehensive approach to multivariate statistical analyses that are important for researchers in all fields of management, including finance, production, accounting, marketing, strategy, technology, and human resources. This book is especially designed to provide doctoral students with a theoretical knowledge of the concepts underlying the most important multivariate techniques and an overview of actual applications. It offers a clear, succinct exposition of each technique with emphasis on when each technique is appropriate and how to use it. This second edition, fully revised, updated, and expanded, reflects the most current evolution in the methods for data analysis in management and the social sciences. In particular, it places a greater emphasis on measurement models, and includes new chapters and sections on: confirmatory factor analysis canonical correlation analysis cluster analysis analysis of covariance structure multi-group confirmatory factor analysis and analysis of covariance structures. Featuring numerous examples, the book may serve as an advanced text or as a resource for applied researchers in industry who want to understand the foundations of the methods and to learn how they can be applied using widely available statistical software.
Statistical Analysis of Operational Risk Data (SpringerBriefs in Statistics)
by Danilo Carità Francesco Martinelli Giovanni De LucaThis concise book for practitioners presents the statistical analysis of operational risk, which is considered the most relevant source of bank risk, after market and credit risk. The book shows that a careful statistical analysis can improve the results of the popular loss distribution approach. The authors identify the risk classes by applying a pooling rule based on statistical tests of goodness-of-fit, use the theory of the mixture of distributions to analyze the loss severities, and apply copula functions for risk class aggregation. Lastly, they assess operational risk data in order to estimate the so-called capital-at-risk that represents the minimum capital requirement that a bank has to hold. The book is primarily intended for quantitative analysts and risk managers, but also appeals to graduate students and researchers interested in bank risks.
Statistical Analysis of Reliability Data (Chapman And Hall/crc Texts In Statistical Science Ser. #27)
by Martin J. CrowderWritten for those who have taken a first course in statistical methods, this book takes a modern, computer-oriented approach to describe the statistical techniques used for the assessment of reliability.
Statistical Analysis with Excel For Dummies
by Joseph SchmullerBecome a stats superstar by using Excel to reveal the powerful secrets of statistics Microsoft Excel offers numerous possibilities for statistical analysis—and you don’t have to be a math wizard to unlock them. In Statistical Analysis with Excel For Dummies, fully updated for the 2021 version of Excel, you’ll hit the ground running with straightforward techniques and practical guidance to unlock the power of statistics in Excel. Bypass unnecessary jargon and skip right to mastering formulas, functions, charts, probabilities, distributions, and correlations. Written for professionals and students without a background in statistics or math, you’ll learn to create, interpret, and translate statistics—and have fun doing it! In this book you’ll find out how to: Understand, describe, and summarize any kind of data, from sports stats to sales figures Confidently draw conclusions from your analyses, make accurate predictions, and calculate correlations Model the probabilities of future outcomes based on past data Perform statistical analysis on any platform: Windows, Mac, or iPad Access additional resources and practice templates through Dummies.com For anyone who’s ever wanted to unleash the full potential of statistical analysis in Excel—and impress your colleagues or classmates along the way—Statistical Analysis with Excel For Dummies walks you through the foundational concepts of analyzing statistics and the step-by-step methods you use to apply them.
Statistical Analysis: The Basics (The Basics)
by Christer ThraneStatistical Analysis: The Basics provides an engaging and easy‑to‑read primer on this sometimes daunting subject. Intended for those with little or no background in mathematics or statistics, this book explores the importance of statistical analysis in the modern world by asking statistical questions about data and explains how to conduct such analyses and correctly interpret the results.Packed with everyday examples from sport, health, education, and leisure, it reinforces the understanding of core topics while avoiding the heavy use of equations and formulae. Written in a highly accessible style and adopting a hands‑on approach, each chapter is accompanied by a summary of key points, illustrations and tables, and recommendations for further reading, with the final chapter delving into the practicalities of conducting a real‑life statistical research project.Statistical Analysis: The Basics is essential reading for anyone who wishes to master the fundamentals of modern‑day statistical analysis.
Statistical Applications for Chemistry, Manufacturing and Controls (CMC) in the Pharmaceutical Industry
by Lanju Zhang Richard K. Burdick David J. Leblond Lori B. Pfahler Jorge Quiroz Leslie Sidor Kimberly VukovinskyThis book examines statistical techniques that are critically important to Chemistry, Manufacturing, and Control (CMC) activities. Statistical methods are presented with a focus on applications unique to the CMC in the pharmaceutical industry. The target audience consists of statisticians and other scientists who are responsible for performing statistical analyses within a CMC environment. Basic statistical concepts are addressed in Chapter 2 followed by applications to specific topics related to development and manufacturing. The mathematical level assumes an elementary understanding of statistical methods. The ability to use Excel or statistical packages such as Minitab, JMP, SAS, or R will provide more value to the reader. The motivation for this book came from an American Association of Pharmaceutical Scientists (AAPS) short course on statistical methods applied to CMC applications presented by four of the authors. One of the course participants asked us for a good reference book, and the only book recommended was written over 20 years ago by Chow and Liu (1995). We agreed that a more recent book would serve a need in our industry. Since we began this project, an edited book has been published on the same topic by Zhang (2016). The chapters in Zhang discuss statistical methods for CMC as well as drug discovery and nonclinical development. We believe our book complements Zhang by providing more detailed statistical analyses and examples.
Statistical Applications for Environmental Analysis and Risk Assessment
by Joseph OfungwuStatistical Applications for Environmental Analysis and Risk Assessment guides readers through real-world situations and the best statistical methods used to determine the nature and extent of the problem, evaluate the potential human health and ecological risks, and design and implement remedial systems as necessary. Featuring numerous worked examples using actual data and "ready-made" software scripts, Statistical Applications for Environmental Analysis and Risk Assessment also includes:* Descriptions of basic statistical concepts and principles in an informal style that does not presume prior familiarity with the subject* Detailed illustrations of statistical applications in the environmental and related water resources fields using real-world data in the contexts that would typically be encountered by practitioners* Software scripts using the high-powered statistical software system, R, and supplemented by USEPA's ProUCL and USDOE's VSP software packages, which are all freely available* Coverage of frequent data sample issues such as non-detects, outliers, skewness, sustained and cyclical trend that habitually plague environmental data samples* Clear demonstrations of the crucial, but often overlooked, role of statistics in environmental sampling design and subsequent exposure risk assessment.
Statistical Arbitrage
by Andrew PoleWhile statistical arbitrage has faced some tough times?as markets experienced dramatic changes in dynamics beginning in 2000?new developments in algorithmic trading have allowed it to rise from the ashes of that fire. Based on the results of author Andrew Pole?s own research and experience running a statistical arbitrage hedge fund for eight years?in partnership with a group whose own history stretches back to the dawn of what was first called pairs trading?this unique guide provides detailed insights into the nuances of a proven investment strategy. Filled with in-depth insights and expert advice, Statistical Arbitrage contains comprehensive analysis that will appeal to both investors looking for an overview of this discipline, as well as quants looking for critical insights into modeling, risk management, and implementation of the strategy.
Statistical Capacity Building
by Thomas K. Morrison Zia Abbasi Noel Atcherley Jaroslav Ku Era Graham L. SlackIMF technical assistance provided by the Statistics Department -- toward assisting IMF member countries in developing the ability to provide reliable and comparable economic and financial data on a timely basis to policymakers and markets -- has increased more than fourfold over the past decade. This assistance has proven critical in countries' building their statistical capacity so as to come into line with international data standards in an increasingly globalized and electronically interconnected world. Statistical Capacity Building: Case Studies and Lessons Learned presents four case studies drawn from experience in three countries in transition to the market, two of which were also in postconflict situations, in the 1990s and early 2000s: Cambodia, Bosnia and Herzegovina, and Ukraine. Issues of setting, institutional and statistical arrangements, strategies, and implementation are examined, and lessons drawn.
Statistical Computing with R, Second Edition (Chapman & Hall/CRC The R Series)
by Maria L. RizzoComputational statistics and statistical computing are two areas that employ computational, graphical, and numerical approaches to solve statistical problems, making the versatile R language an ideal computing environment for these fields. This second edition continues to encompass the traditional core material of computational statistics, with an
Statistical Data Analysis and Entropy (Behaviormetrics: Quantitative Approaches to Human Behavior #3)
by Nobuoki EshimaThis book reconsiders statistical methods from the point of view of entropy, and introduces entropy-based approaches for data analysis. Further, it interprets basic statistical methods, such as the chi-square statistic, t-statistic, F-statistic and the maximum likelihood estimation in the context of entropy. In terms of categorical data analysis, the book discusses the entropy correlation coefficient (ECC) and the entropy coefficient of determination (ECD) for measuring association and/or predictive powers in association models, and generalized linear models (GLMs). Through association and GLM frameworks, it also describes ECC and ECD in correlation and regression analyses for continuous random variables. In multivariate statistical analysis, canonical correlation analysis, T2-statistic, and discriminant analysis are discussed in terms of entropy. Moreover, the book explores the efficiency of test procedures in statistical tests of hypotheses using entropy. Lastly, it presents an entropy-based path analysis for structural GLMs, which is applied in factor analysis and latent structure models. Entropy is an important concept for dealing with the uncertainty of systems of random variables and can be applied in statistical methodologies. This book motivates readers, especially young researchers, to address the challenge of new approaches to statistical data analysis and behavior-metric studies.
Statistical Data Mining Using SAS Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
by George FernandezStatistical Data Mining Using SAS Applications, Second Edition describes statistical data mining concepts and demonstrates the features of user-friendly data mining SAS tools. Integrating the statistical and graphical analysis tools available in SAS systems, the book provides complete statistical data mining solutions without writing SAS program co
Statistical Data Mining and Knowledge Discovery
by Hamparsum BozdoganMassive data sets pose a great challenge to many cross-disciplinary fields, including statistics. The high dimensionality and different data types and structures have now outstripped the capabilities of traditional statistical, graphical, and data visualization tools. Extracting useful information from such large data sets calls for novel approache
Statistical Decision Problems
by Michael Zabarankin Stan UryasevStatistical Decision Problems presents a quick and concise introduction into the theory of risk, deviation and error measures that play a key role in statistical decision problems. It introduces state-of-the-art practical decision making through twenty-one case studies from real-life applications. The case studies cover a broad area of topics and the authors include links with source code and data, a very helpful tool for the reader. In its core, the text demonstrates how to use different factors to formulate statistical decision problems arising in various risk management applications, such as optimal hedging, portfolio optimization, cash flow matching, classification, and more. The presentation is organized into three parts: selected concepts of statistical decision theory, statistical decision problems, and case studies with portfolio safeguard. The text is primarily aimed at practitioners in the areas of risk management, decision making, and statistics. However, the inclusion of a fair bit of mathematical rigor renders this monograph an excellent introduction to the theory of general error, deviation, and risk measures for graduate students. It can be used as supplementary reading for graduate courses including statistical analysis, data mining, stochastic programming, financial engineering, to name a few. The high level of detail may serve useful to applied mathematicians, engineers, and statisticians interested in modeling and managing risk in various applications.
Statistical Design of Experiments with Engineering Applications
by Kamel Rekab Muzaffar ShaikhIn today's high-technology world, with flourishing e-business and intense competition at a global level, the search for the competitive advantage has become a crucial task of corporate executives. Quality, formerly considered a secondary expense, is now universally recognized as a necessary tool. Although many statistical methods are available for
Statistical Inference as a Bargaining Game
by Eduardo LeyA report from the International Monetary Fund.
Statistical Inference for Financial Engineering
by Masanobu Taniguchi Tomoyuki Amano Hiroaki Ogata Hiroyuki TaniaiThis monograph provides the fundamentals of statistical inference for financial engineering and covers some selected methods suitable for analyzing financial time series data. In order to describe the actual financial data, various stochastic processes, e. g. non-Gaussian linear processes, non-linear processes, long-memory processes, locally stationary processes etc. are introduced and their optimal estimation is considered as well. This book also includes several statistical approaches, e. g. , discriminant analysis, the empirical likelihood method, control variate method, quantile regression, realized volatility etc. , which have been recently developed and are considered to be powerful tools for analyzing the financial data, establishing a new bridge between time series and financial engineering. This book is well suited as a professional reference book on finance, statistics and statistical financial engineering. Readers are expected to have an undergraduate-level knowledge of statistics.
Statistical Inference via Data Science: A ModernDive into R and the Tidyverse (Chapman & Hall/CRC The R Series)
by Chester Ismay Albert Y. Kim Arturo ValdiviaStatistical Inference via Data Science: A ModernDive into R and the Tidyverse, Second Edition offers a comprehensive guide to learning statistical inference with data science tools widely used in industry, academia, and government. The first part of this book introduces the tidyverse suite of R packages, including ggplot2 for data visualization and dplyr for data wrangling. The second part introduces data modeling via simple and multiple linear regression. The third part presents statistical inference using simulation-based methods within a general framework implemented in R via the infer package, a suitable complement to the tidyverse. By working with these methods, readers can implement effective exploratory data analyses, conduct statistical modeling with data, and carry out statistical inference via confidence intervals and hypothesis testing. All of these tasks are performed by strongly emphasizing data visualization.Key Features in the Second Edition: Minimal Prerequisites: No prior calculus or coding experience is needed, making the content accessible to a wide audience. Real-World Data: Learn with real-world datasets, including all domestic flights leaving New York City in 2023, the Gapminder project, FiveThirtyEight.com data, and new datasets on health, global development, music, coffee quality, and geyser eruptions. Simulation-Based Inference: Statistical inference through simulation-based methods. Expanded Theoretical Discussions: Includes deeper coverage of theory-based approaches, their connection with simulation-based approaches, and a presentation of intuitive and formal aspects of these methods. Enhanced Use of the infer Package: Leverages the infer package for “tidy” and transparent statistical inference, enabling readers to construct confidence intervals and conduct hypothesis tests through multiple linear regression and beyond. Dynamic Online Resources: All code and output are embedded in the text, with additional interactive exercises, discussions, and solutions available online. Broadened Applications: Suitable for undergraduate and graduate courses, including statistics, data science, and courses emphasizing reproducible research. The first edition of the book has been used in so many different ways--for courses in statistical inference, statistical programming, business analytics, and data science for social policy, and by professionals in many other means. Ideal for those new to statistics or looking to deepen their knowledge, this edition provides a clear entry point into data science and modern statistical methods.
Statistical Issues in Allocating Funds by Formula
by Panel on Formula AllocationsIn 2000, the federal government distributed over $260 billion of funding to state and local governments via 180 formula programs. These programs promote a wide spectrum of economic and social objectives, such as improving educational outcomes and increasing accessibility to medical care, and many are designed to compensate for differences in fiscal capacity that affect governments’ abilities to address identified needs. Large amounts of state revenues are also distributed through formula allocation programs to counties, cities, and other jurisdictions. Statistical Issues in Allocating Funds by Formula identifies key issues concerning the design and use of these formulas and advances recommendations for improving the process. In addition to the more narrow issues relating to formula design and input data, the book discusses broader issues created by the interaction of the political process and the use of formulas to allocate funds.Statistical Issues in Allocating Funds by Formula is only up-to-date guide for policymakers who design fund allocation programs. Congress members who are crafting legislation for these programs and federal employees who are in charge of distributing the funds will find this book indispensable.
Statistical Learning Using Neural Networks: A Guide for Statisticians and Data Scientists with Python
by Basilio de Braganca Pereira Calyampudi Radhakrishna Rao Fabio Borges de OliveiraStatistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks starting from the basics and increasingly demanding more effort from readers, who can learn the theory and its applications in statistical methods with concrete Python code examples. It presents a wide range of widely used statistical methodologies, applied in several research areas with Python code examples, which are available online. It is suitable for scientists and developers as well as graduate students.Key Features: Discusses applications in several research areas Covers a wide range of widely used statistical methodologies Includes Python code examples Gives numerous neural network models This book covers fundamental concepts on Neural Networks including Multivariate Statistics Neural Networks, Regression Neural Network Models, Survival Analysis Networks, Time Series Forecasting Networks, Control Chart Networks, and Statistical Inference Results. This book is suitable for both teaching and research. It introduces neural networks and is a guide for outsiders of academia working in data mining and artificial intelligence (AI). This book brings together data analysis from statistics to computer science using neural networks.
Statistical Learning and Data Science
by Fionn Murtagh Mireille Gettler Summa Léon Bottou Bernard Goldfarb Catherine Pardoux Myriam TouatiData analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream. Unsupervised data analysis, including cluster analysis, factor analysis, and low dimensionality mapping methods continually being updated, have reached new heights of achievement in the incredibly rich data wor