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Designed for instructors who want to stress the understanding of basic concepts and the development of "statistical intuition," this book demonstrates that statistical reasoning is everywhere and that statistical concepts are as important to students' personal lives as they are to their future professional careers. Ramsey aims to develop statistically literacy - from the ability to read and think critically about statistics published in popular media to the ability to analyze and act upon statistics gathered in the business world. The underlying philosophy of this book is that given a reasonable level of depth in the analysis, the student can later acquire a much more extensive, and even more intensive, exposure to statistics on their own or in the context of the work environment. Some use of calculus is included. Use of the computer is integrated throughout.
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.
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.
The purpose of the Review is to promote the collection, criticism, and interpretation of economic statistics, with a view to making them more accurate and valuable than they are at present for business and scientific purposes.
"Interpreting Economic and Social Data" aims at rehabilitating the descriptive function of socio-economic statistics, bridging the gap between today's statistical theory on one hand, and econometric and mathematical models of society on the other. It does this by offering a deeper understanding of data and methods with surprising insights, the result of the author's six decades of teaching, consulting and involvement in statistical surveys. The author challenges many preconceptions about aggregation, time series, index numbers, frequency distributions, regression analysis and probability, nudging statistical theory in a different direction. "Interpreting Economic and Social Data" also links statistics with other quantitative fields like accounting and geography. This book is aimed at students and professors in business, economics demographic and social science courses, and in general, at users of socio-economic data, requiring only an acquaintance with elementary statistical theory.