Das Schreiben einer quantitativ-empirischen Graduierungsarbeit ist wie das Kochen in einer Mensa. Es sollte schnell gehen, das Essen schmecken, gesund und kostengunstig sein. Um das zu erreichen, mussen Rezepte eingehalten werden. Ohne Leidenschaft, aber professionell. Dieses essential gibt Ihnen solche Rezepte an die Hand, die Sie nur nachkochen mussen. Wie ist eine solche Arbeit aufgebaut? Wie formuliere ich Hypothesen und wie uberprufe ich diese korrekt? Es wird der Umgang mit der kostenfreien Statistiksoftware "R" erklart. Die benoetigte Syntax finden Sie im essential. Sie mussen sie lediglich kopieren.

Best-worst scaling (BWS) is an extension of the method of paired comparison to multiple choices that asks participants to choose both the most and the least attractive options or features from a set of choices. It is an increasingly popular way for academics and practitioners in social science, business, and other disciplines to study and model choice. This book provides an authoritative and systematic treatment of best-worst scaling, introducing readers to the theory and methods for three broad classes of applications. It uses a variety of case studies to illustrate simple but reliable ways to design, implement, apply, and analyze choice data in specific contexts, and showcases the wide range of potential applications across many different disciplines. Best-worst scaling avoids many rating scale problems and will appeal to those wanting to measure subjective quantities with known measurement properties that can be easily interpreted and applied.

Highlights the pitfalls of data analysis and emphasizes the importance of using the appropriate metrics before making key decisions. Big data is often touted as the key to understanding almost every aspect of contemporary life. This critique of "information hubris" shows that even more important than data is finding the right metrics to evaluate it. The author, an expert in environmental design and city planning, examines the many ways in which we measure ourselves and our world. He dissects the metrics we apply to health, worker productivity, our children's education, the quality of our environment, the effectiveness of leaders, the dynamics of the economy, and the overall well-being of the planet. Among the areas where the wrong metrics have led to poor outcomes, he cites the fee-for-service model of health care, corporate cultures that emphasize time spent on the job while overlooking key productivity measures, overreliance on standardized testing in education to the detriment of authentic learning, and a blinkered focus on carbon emissions, which underestimates the impact of industrial damage to our natural world. He also examines various communities and systems that have achieved better outcomes by adjusting the ways in which they measure data. The best results are attained by those that have learned not only what to measure and how to measure it, but what it all means. By highlighting the pitfalls inherent in data analysis, this illuminating book reminds us that not everything that can be counted really counts.

Die Arbeit untersucht multiparadigmatisch das fur Forschung & Entwicklung spezifische Bilanzierungsverhalten. Ausgangspunkt ist eine normendeskriptive Analyse der abschlusspolitischen Gestaltungsspielraume nach internationalen und handelsrechtlichen Rechnungslegungsvorschriften. In einer breitangelegten deskriptiv-empirischen Studie wird das F&E-spezifische Bilanzierungsverhalten deutscher boersennotierter Unternehmen untersucht. Dabei erstaunt, wie viele Unternehmen ihren Offenlegungspflichten nicht in vollem Umfang nachkommen. Im Rahmen der sich anschliessenden positiv-empirischen Studie werden die Determinanten des F&E-spezifischen Bilanzierungsverhaltens umfassend eruiert. Die Ergebnisse lassen vermuten, dass das Bilanzierungsverhalten massgeblich durch die Risikoaversion der Manager beeinflusst wird.

How to Divide When There Isn't Enough develops a rigorous yet accessible presentation of the state-of-the-art for the adjudication of conflicting claims and the theory of taxation. It covers all aspects one may wish to know about claims problems: the most important rules, the most important axioms, and how these two sets are related. More generally, it also serves as an introduction to the modern theory of economic design, which in the last twenty years has revolutionized many areas of economics, generating a wide range of applicable allocations rules that have improved people's lives in many ways. In developing the theory, the book employs a variety of techniques that will appeal to both experts and non-experts. Compiling decades of research into a single framework, William Thomson provides numerous applications that will open a large number of avenues for future research.

Dieses Essential fuhrt uber die formale Methode des statistischen Entscheidens hinaus und klart die Frage, mit welcher Gewissheit der Ausgang eines Tests ein Problem loesen kann. Wer sich in kurzer Zeit ein Grundverstandnis der Beurteilenden Statistik erwerben will, kommt an diesem Buch nicht vorbei. Es setzt am Ursprung der Stichprobentheorie an, erklart die Bayes-Evidenzmasse des Hypothesentests und diskutiert ihre Wirksamkeit an allgemeingultigen Fallen. Der Stoff des Essentials bildet einen wissenschaftlichen Pfad zur Kunstlichen Intelligenz, der dem Leser in origineller Weise den objektorientierten Entwurf eines kunstlichen Entscheidungsagenten darlegt und Einblicke in den Bau von Softwarekomponenten bietet.

This book reviews the three most popular methods (and their extensions) in applied economics and other social sciences: matching, regression discontinuity, and difference in differences. The book introduces the underlying econometric/statistical ideas, shows what is identified and how the identified parameters are estimated, and then illustrates how they are applied with real empirical examples. The book emphasizes how to implement the three methods with data: many data and programs are provided in the online appendix. All readers--theoretical econometricians/statisticians, applied economists/social-scientists and researchers/students--will find something useful in the book from different perspectives.

Dieses Buch fuhrt anwendungsorientiert in die Beschreibende und Schliessende Statistik, in die Wahrscheinlichkeitsrechnung und in die Stochastische Modellierung ein und wendet sich insbesondere an Studierende der Informatik, des Ingenieur- und Wirtschaftsingenieurwesens sowie der Wirtschaftswissenschaften. Es ist ein idealer Begleiter zu jeder einsemestrigen Grundvorlesung in Statistik: Die Autoren stellen die wesentlichen Inhalte und Aspekte in kurzer und pragnanter Form dar und verzichten bewusst auf ausfuhrliche Motivationen. Zur UEberprufung des eigenen Wissens stehen Beispielaufgaben mit detaillierten Loesungen zur Verfugung. Die durchgesehene Neuauflage wurde um etwa 50 Multiple-Choice-Fragen erweitert. Diese koennen mit der Springer-Nature-Flashcards-App kostenlos heruntergeladen und interaktiv genutzt werden und sind auszugsweise auch im Buch enthalten. Auf diese Weise kann das eigene Verstandnis der Inhalte getestet werden.

The papers in this volume analyze the deployment of Big Data to solve both existing and novel challenges in economic measurement. The existing infrastructure for the production of key economic statistics relies heavily on data collected through sample surveys and periodic censuses, together with administrative records generated in connection with tax administration. The increasing difficulty of obtaining survey and census responses threatens the viability of existing data collection approaches. The growing availability of new sources of Big Data-such as scanner data on purchases, credit card transaction records, payroll information, and prices of various goods scraped from the websites of online sellers-has changed the data landscape. These new sources of data hold the promise of allowing the statistical agencies to produce more accurate, more disaggregated, and more timely economic data to meet the needs of policymakers and other data users. This volume documents progress made toward that goal and the challenges to be overcome to realize the full potential of Big Data in the production of economic statistics. It describes the deployment of Big Data to solve both existing and novel challenges in economic measurement, and it will be of interest to statistical agency staff, academic researchers, and serious users of economic statistics.

Dieses Lehrbuch vermittelt anwendungsorientiert die Verfahren der Wahrscheinlichkeitsrechnung und Induktiven Statistik. Anhand zahlreicher Beispiele werden die statistischen Methoden nicht nur anschaulich dargestellt, sondern ihre Ergebnisse auch ausfuhrlich interpretiert. Somit eignet sich das Buch hervorragend als Begleitlekture und zum selbststandigen Nacharbeiten einer Vorlesung oder auch zum gezielten Nachschlagen bestimmter Fragestellungen. Es empfiehlt sich auch fur Praktiker, beispielsweise aus der Markt- und Meinungsforschung und dem Controlling, die sich uber die Durchfuhrung und Interpretation von statistischen Tests sowie die Berechnung von Konfidenzintervallen informieren wollen.

As one of the first texts to take a behavioral approach to macroeconomic expectations, this book introduces a new way of doing economics. Roetheli uses cognitive psychology in a bottom-up method of modeling macroeconomic expectations. His research is based on laboratory experiments and historical data, which he extends to real-world situations. Pattern extrapolation is shown to be the key to understanding expectations of inflation and income. The quantitative model of expectations is used to analyze the course of inflation and nominal interest rates in a range of countries and historical periods. The model of expected income is applied to the analysis of business cycle phenomena such as the great recession in the United States. Data and spreadsheets are provided for readers to do their own computations of macroeconomic expectations. This book offers new perspectives in many areas of macro and financial economics.

In diesem Lehrbuch werden einerseits die grundlegenden Begriffe und Verfahren der deskriptiven und induktiven Statistik dargestellt, an Beispielen erklart und durch Aufgaben mit ausfuhrlichen Loesungen eingeubt. Andererseits soll die fur ein Statistikbuch recht ausfuhrliche Verbalisierung - auch in Form von Management Summaries - dabei helfen, statistische Erhebungs- und Auswertungsprozeduren kritisch zu reflektieren und so den Informationsgehalt von statistischen Ergebnissen im Entscheidungszusammenhang bzw. bei der UEberprufung von Hypothesen werten zu koennen. Die 6. Auflage wurde aktualisiert und um einen zentralen Aufgabenblock sowohl aus der deskriptiven als auch aus der induktiven Statistik erganzt. Das Lehrbuch ist so konzipiert, dass eine Quer- und Langsschnittsintegration in die meisten Bachelor-Curricula fur Wirtschaftswissenschaftler an deutschsprachigen Hochschulen gewahrleistet ist. Zusatzliche Lern- und Prasentationshilfen im Internet erleichtern das Selbststudium und den Einsatz des Buches als Erganzungslekture zu einer entsprechenden Lehrveranstaltung.

The Oxford Handbook of Panel Data examines new developments in the theory and applications of panel data. It includes basic topics like non-stationary panels, co-integration in panels, multifactor panel models, panel unit roots, measurement error in panels, incidental parameters and dynamic panels, spatial panels, nonparametric panel data, random coefficients, treatment effects, sample selection, count panel data, limited dependent variable panel models, unbalanced panel models with interactive effects and influential observations in panel data. Contributors to the Handbook explore applications of panel data to a wide range of topics in economics, including health, labor, marketing, trade, productivity, and macro applications in panels. This Handbook is an informative and comprehensive guide for both those who are relatively new to the field and for those wishing to extend their knowledge to the frontier. It is a trusted and definitive source on panel data, having been edited by Professor Badi Baltagi-widely recognized as one of the foremost econometricians in the area of panel data econometrics. Professor Baltagi has successfully recruited an all-star cast of experts for each of the well-chosen topics in the Handbook.

In this best-of-breed study guide, leading expert Michael Gregg helps you master all the topics you need to know to succeed on your Certified Ethical Hacker Version 9 exam and advance your career in IT security. Michael's concise, focused approach explains every exam objective from a real-world perspective, helping you quickly identify weaknesses and retain everything you need to know. * Every feature of this book supports both efficient exam preparation and long-term mastery: * Opening Topics Lists identify the topics you need to learn in each chapter and list EC-Council's official exam objectives * Key Topics figures, tables, and lists call attention to the information that's most crucial for exam success * Exam Preparation Tasks enable you to review key topics, complete memory tables, define key terms, work through scenarios, and answer review questions...going beyond mere facts to master the concepts that are crucial to passing the exam and enhancing your career * Key Terms are listed in each chapter and defined in a complete glossary, explaining all the field's essential terminology This study guide helps you master all the topics on the latest CEH exam, including * Ethical hacking basics * Technical foundations of hacking * Footprinting and scanning * Enumeration and system hacking * Linux distro's, such as Kali and automated assessment tools * Trojans and backdoors * Sniffers, session hijacking, and denial of service * Web server hacking, web applications, and database attacks * Wireless technologies, mobile security, and mobile attacks * IDS, firewalls, and honeypots * Buffer overflows, viruses, and worms * Cryptographic attacks and defenses * Cloud security and social engineering

Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.

A variety of different social, natural, and technological systems can be described by the same mathematical framework. This holds from the Internet to food webs and to boards of company directors. In all these situations a graph of the elements of the system and their interconnections displays a universal feature. There are only few elements with many connections, and many elements with few connections. This book presents the experimental evidence of these 'scale-free networks' and provides students and researchers with a corpus of theoretical results and algorithms to analyse and understand these features. The content of this book and the exposition makes it a clear textbook for beginners, and a reference book for the experts.

This book integrates the fundamentals of asymptotic theory of statistical inference for time series under nonstandard settings, e.g., infinite variance processes, not only from the point of view of efficiency but also from that of robustness and optimality by minimizing prediction error. This is the first book to consider the generalized empirical likelihood applied to time series models in frequency domain and also the estimation motivated by minimizing quantile prediction error without assumption of true model. It provides the reader with a new horizon for understanding the prediction problem that occurs in time series modeling and a contemporary approach of hypothesis testing by the generalized empirical likelihood method. Nonparametric aspects of the methods proposed in this book also satisfactorily address economic and financial problems without imposing redundantly strong restrictions on the model, which has been true until now. Dealing with infinite variance processes makes analysis of economic and financial data more accurate under the existing results from the demonstrative research. The scope of applications, however, is expected to apply to much broader academic fields. The methods are also sufficiently flexible in that they represent an advanced and unified development of prediction form including multiple-point extrapolation, interpolation, and other incomplete past forecastings. Consequently, they lead readers to a good combination of efficient and robust estimate and test, and discriminate pivotal quantities contained in realistic time series models.

Dieses Lehrbuch fuhrt anschaulich in die klassischen Gebiete der Statistik ein. Je nach Vorkenntnissen und Interesse finden Leser neben leicht verstandlichen Erklarungen auch mathematische Herleitungen. Der Autor stellt die Beurteilung von Voraussetzungen und Rahmenbedingungen sowie die Interpretation der Ergebnisse in den Vordergrund statt nur Statistik-Rezepturen zu vermitteln. Trotz hoher Verstandlichkeit beschrankt sich die Themenauswahl keineswegs auf einfache Inhalte, sondern orientiert sich an den Anforderungen der akademischen Ausbildung. Empfehlenswert fur Studierende der Wirtschafts- und Sozialwissenschaften sowie fur Interessenten anderer Fachgebiete. Die Themenauswahl berucksichtigt insbesondere den praktischen Bezug zu Beruf und Alltag.