Dieses Buch vermittelt anschaulich und leicht verstandlich die Grundlagen der Wirtschaftsstatistik (Mathematische Voraussetzungen, Beschreibende und Schliessende Statistik, Datenanalyse), die fur empirische Aufgabenstellungen, Datenaufbereitung, Ergebnisinterpretation und den Einsatz von Statistikprogrammen wie SPSS, Excel und NSDstat notwendig sind.
Die relevanten Konzepte und Verfahren werden im Rahmen konkreter, praxisbezogener Datenanalysen vorgestellt. Ausfuhrliche Ubungsaufgaben am jeweiligen Kapitelende und Musterlosungen im Anhang ermoglichen ein effizientes Selbststudium sowie eine erfolgreiche Prufungsvorbereitung. Die korrigierte 3. Auflage wurde sorgfaltig durchgesehen und uberarbeitet

If you follow the headlines, you could be forgiven for thinking that things in Ireland are worse than ever. In fact, we live longer than ever before, we have never been healthier or better educated, we earn five times more than our grandparents did, our personal freedoms exceed those of any previous generation, and the lives of women and children have been transformed for the better. At a time when some good news is welcome, this uplifting book tells our national story through facts and stats, placing Ireland under the microscope to chart 100 undeniable achievements of the past 100 years. When the State was formed, Ireland was one of the most poverty-stricken nations in Europe. Now it has the second-highest quality of life in the world. While there is still more to be done, In Fact illustrates that Ireland, for all its imperfections, is in a much better state than you might think.

1. This book is applicable to courses across the social and behavioral science on a wide range of quantitative methods courses. 2. The book is based solely on Stata for EFA - one of the top statistics software packages used in behavioral and social sciences. 3. Clear step-by-step guidance combined with screen shots to show how to apply EFA to real data.

Statistical Programming in SAS Second Edition provides a foundation for programming to implement statistical solutions using SAS, a system that has been used to solve data analytic problems for more than 40 years. The author includes motivating examples to inspire readers to generate programming solutions. Upper-level undergraduates, beginning graduate students, and professionals involved in generating programming solutions for data-analytic problems will benefit from this book. The ideal background for a reader is some background in regression modeling and introductory experience with computer programming. The coverage of statistical programming in the second edition includes Getting data into the SAS system, engineering new features, and formatting variables Writing readable and well-documented code Structuring, implementing, and debugging programs that are well documented Creating solutions to novel problems Combining data sources, extracting parts of data sets, and reshaping data sets as needed for other analyses Generating general solutions using macros Customizing output Producing insight-inspiring data visualizations Parsing, processing, and analyzing text Programming solutions using matrices and connecting to R Processing text Programming with matrices Connecting SAS with R Covering topics that are part of both base and certification exams.

Introduction to Functional Data Analysis provides a concise textbook introduction to the field. It explains how to analyze functional data, both at exploratory and inferential levels. It also provides a systematic and accessible exposition of the methodology and the required mathematical framework. The book can be used as textbook for a semester-long course on FDA for advanced undergraduate or MS statistics majors, as well as for MS and PhD students in other disciplines, including applied mathematics, environmental science, public health, medical research, geophysical sciences and economics. It can also be used for self-study and as a reference for researchers in those fields who wish to acquire solid understanding of FDA methodology and practical guidance for its implementation. Each chapter contains plentiful examples of relevant R code and theoretical and data analytic problems. The material of the book can be roughly divided into four parts of approximately equal length: 1) basic concepts and techniques of FDA, 2) functional regression models, 3) sparse and dependent functional data, and 4) introduction to the Hilbert space framework of FDA. The book assumes advanced undergraduate background in calculus, linear algebra, distributional probability theory, foundations of statistical inference, and some familiarity with R programming. Other required statistics background is provided in scalar settings before the related functional concepts are developed. Most chapters end with references to more advanced research for those who wish to gain a more in-depth understanding of a specific topic.

Quantile regression constitutes an ensemble of statistical techniques intended to estimate and draw inferences about conditional quantile functions. Median regression, as introduced in the 18th century by Boscovich and Laplace, is a special case. In contrast to conventional mean regression that minimizes sums of squared residuals, median regression minimizes sums of absolute residuals; quantile regression simply replaces symmetric absolute loss by asymmetric linear loss. Since its introduction in the 1970's by Koenker and Bassett, quantile regression has been gradually extended to a wide variety of data analytic settings including time series, survival analysis, and longitudinal data. By focusing attention on local slices of the conditional distribution of response variables it is capable of providing a more complete, more nuanced view of heterogeneous covariate effects. Applications of quantile regression can now be found throughout the sciences, including astrophysics, chemistry, ecology, economics, finance, genomics, medicine, and meteorology. Software for quantile regression is now widely available in all the major statistical computing environments. The objective of this volume is to provide a comprehensive review of recent developments of quantile regression methodology illustrating its applicability in a wide range of scientific settings. The intended audience of the volume is researchers and graduate students across a diverse set of disciplines.

The publication contains detailed official national accounts data for over 200 countries or areas of the World for the years 2001 to 2012. It is a valuable source of information on the state and structure of economies worldwide. The data for each country or area are presented in separate chapters with uniform table headings and classifications as recommended in the System of National Accounts 1993 (1993 SNA). Each country chapter also contains a write-up on the methodology and data sources which are used to compile the national accounts. A summary of the SNA conceptual framework, classifications, definitions, is also included in the publication. It contains statistics on gross domestic product by expenditure at current and constant prices and the relations among product, income, saving and net lending; value added by kind of activity at current and constant prices, and output, gross value added and fixed assets by industry; classification of consumption expenditure of general government, households, and non-profit institutions serving households according to purpose; production account through the financial account for the institutional sectors; and cross classification of gross value added by industry and institutional sector.

The demand for high quality detailed public finance statistics covering a globally representative sample of countries has increased dramatically during the recent financial crisis. Due to the complexity of public finance statistics, however, such data tend to be either available in oversimplified high level aggregates and lacking in methodological transparency, or, available with a great level of detail and a unified methodological approach yet overly complicated to understand. The IMF's Government Finance Statistics Yearbook shows fiscal data of around 140 countries following the Government Finance Statistics Manual 2001 framework. The associated database includes data series covering over an almost 40 year period. The IMF's Statistics Department embarked on several initiatives to improve its accessibility