This brand new book in statistics aims to provide an introduction to the key methods and techniques essential to a typical statistics syllabus, whilst also helping students to develop the skills needed to analyse, interpret and prepare data for use in business, economics and related disciplines. Covering the essential methods required at undergraduate level, the book is structured into four parts that deal with descriptive statistics, probability, sample theory and inferential statistics, taking students from the basics through to more advanced topics such as multiple linear regression. Every chapter contains clear descriptions of each technique, illustrated with numerous worked examples to aid students in understanding how to practice statistical methods. The real data used in the examples is drawn from European sources. The text also contains longer case examples set in a European business context, to show how statistics is used everyday in the business environment. Finally, each chapter concludes with a variety of exercises to test students’ ability to apply the theory and attain a high level of competence in using statistics. This comprehensive book is ideal for student of statistics at undergraduate level taking an introductory module in the topic.

This textbook introduces readers to practical statistical issues by presenting them within the context of real-life economics and business situations. It presents the subject in a non-threatening manner, with an emphasis on concise, easily understandable explanations. It has been designed to be accessible and student-friendly and, as an added learning feature, provides all the relevant data required to complete the accompanying exercises and computing problems, which are presented at the end of each chapter. It also discusses index numbers and inequality indices in detail, since these are of particular importance to students and commonly omitted in textbooks. Throughout the text it is assumed that the student has no prior knowledge of statistics. It is aimed primarily at business and economics undergraduates, providing them with the basic statistical skills necessary for further study of their subject. However, students of other disciplines will also find it relevant.

Applied Statistical Methods covers the fundamental understanding of statistical methods necessary to deal with a wide variety of practical problems. This 14-chapter text presents the topics covered in a manner that stresses clarity of understanding, interpretation, and method of application. The introductory chapter illustrates the importance of statistical analysis. The next chapters introduce the methods of data summarization, including frequency distributions, cumulative frequency distributions, and measures of central tendency and variability. These topics are followed by discussions of the fundamental principles of probability, the concepts of sample spaces, outcomes, events, probability, independence of events, and the characterization of discrete and continuous random variables. Other chapters explore the distribution of several important statistics; statistical tests of hypotheses; point and interval estimation; and simple linear regression. The concluding chapters review the elements of single- and two-factor analysis of variance and the design of analysis of variance experiments. This book is intended primarily for advanced undergraduate and graduate students in the mathematical, physical, and engineering sciences, as well as in economics, business, and related areas. Researchers and line personnel in industry and government will find this book useful in self-study.

Introduction.Big data for twenty-first-century economic statistics: the future is now /Katharine G. Abraham, Ron S. Jarmin, Brian C. Moyer, and Matthew D. Shapiro --Toward comprehensive use of big data in economic statistics.Reengineering key national economic indicators /Gabriel Ehrlich, John Haltiwanger, Ron S. Jarmin, David Johnson, and Matthew D. Shapiro ;Big data in the US consumer price index: experiences and plans /Crystal G. Konny, Brendan K. Williams, and David M. Friedman ;Improving retail trade data products using alternative data sources /Rebecca J. Hutchinson ;From transaction data to economic statistics: constructing real-time, high-frequency, geographic measures of consumer spending /Aditya Aladangady, Shifrah Aron-Dine, Wendy Dunn, Laura Feiveson, Paul Lengermann, and Claudia Sahm ;Improving the accuracy of economic measurement with multiple data sources: the case of payroll employment data /Tomaz Cajner, Leland D. Crane, Ryan A. Decker, Adrian Hamins-Puertolas, and Christopher Kurz --Uses of big data for classification.Transforming naturally occurring text data into economic statistics: the case of online job vacancy postings /Arthur Turrell, Bradley Speigner, Jyldyz Djumalieva, David Copple, and James Thurgood ;Automating response evaluation for franchising questions on the 2017 economic census /Joseph Staudt, Yifang Wei, Lisa Singh, Shawn Klimek, J. Bradford Jensen, and Andrew Baer ;Using public data to generate industrial classification codes /John Cuffe, Sudip Bhattacharjee, Ugochukwu Etudo, Justin C. Smith, Nevada Basdeo, Nathaniel Burbank, and Shawn R. Roberts --Uses of big data for sectoral measurement.Nowcasting the local economy: using Yelp data to measure economic activity /Edward L. Glaeser, Hyunjin Kim, and Michael Luca ;Unit values for import and export price indexes: a proof of concept /Don A. Fast and Susan E. Fleck ;Quantifying productivity growth in the delivery of important episodes of care within the Medicare program using insurance claims and administrative data /John A. Romley, Abe Dunn, Dana Goldman, and Neeraj Sood ;Valuing housing services in the era of big data: a user cost approach leveraging Zillow microdata /Marina Gindelsky, Jeremy G. Moulton, and Scott A. Wentland --Methodological challenges and advances.Off to the races: a comparison of machine learning and alternative data for predicting economic indicators /Jeffrey C. Chen, Abe Dunn, Kyle Hood, Alexander Driessen, and Andrea Batch ;A machine learning analysis of seasonal and cyclical sales in weekly scanner data /Rishab Guha and Serena Ng ;Estimating the benefits of new products /W. Erwin Diewert and Robert C. Feenstra.

This book is intended as a textbook for a first course in applied statistics for students of economics, public administration and business administration. A limited knowledge of mathematics and - in one single chapter - some knowledge of elementary matrix algebra is required for understanding the text. Complicated mathematical proofs are avoided and the explanations are based on intuition and numerical examples. The aim of this book is to enable the student to understand the reasoning underlying a statistical analysis and to apply statistical methods to problems likely to be met within the fields of economics, public administration and business administration. The topics covered by the book are: - methods for exploratory data analysis - probability theory and standard statistical distributions - statistical inference theory - and three main areas of application: regression analysis, survey sampling and contingency tables. The treatment of exploratory data analysis, regression analysis and the analysis of contingency tables are based on the most recent theoretical developments in these areas. Most of the examples have never been presented before in English textbooks.

Why make statistics harder than it has to be? Lind/Marchal/Mason: STATISTICAL TECHNIQUES IN BUSINESS AND ECONOMICS, 11/e is a perennial market best seller due to its comprehensive coverage of statistical tools and methods delivered in a student friendly, step-by-step format. The text is non-threatening and presents concepts clearly and succinctly with a conversational writing style. All statistical concepts are illustrated with solved applied examples immediately upon introduction. Modern computing tools and applications are introduced, but the text maintains a focus on presenting statistics content as oppose to technology or programming methods, and the eleventh edition continues as a ‘students’ text with increased emphasis on interpretation of data and results.lts.