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Every time you buy a can of tuna or a new television, its bar code is scanned to record its price and other information. These "scanner data" offer a number of attractive features for economists and statisticians, because they are collected continuously, are available quickly, and record prices for all items sold, not just a statistical sample. But scanner data also present a number of difficulties for current statistical systems. Scanner Data and Price Indexes assesses both the promise and the challenges of using scanner data to produce economic statistics. Three papers present the results of work in progress at statistical agencies in the U.S., United Kingdom, and Canada, including a project at the U.S. Bureau of Labor Statistics to investigate the feasibility of incorporating scanner data into the monthly Consumer Price Index. Other papers demonstrate the enormous potential of using scanner data to test economic theories and estimate the parameters of economic models, and provide solutions for some of the problems that arise when using scanner data, such as dealing with missing data.
Using Scanner Data for Food Policy Research is a practitioners' guide to using and interpreting scanner data obtained from stores and households in policy research. It provides practical advice for using the data and interpreting their results. It helps the reader address key methodological issues such as aggregation, constructing price indices, and matching the data to nutrient values. It demonstrates some of the key econometric and statistical applications of the data, including estimating demand systems for policy simulation, analyzing effects of food access on food choices, and conducting cost-benefit analysis of food policies. This guide is intended for early-career researchers, particularly those working with scanner data in agricultural and food economics, nutrition, and public health contexts. - Describe different types of scanner data, the types of information available in the data, and the vendors that offer these data - Describe food-label data that can be appended to scanner data - Identify key questions that researchers should consider when acquiring scanner and label data for food policy research - Demonstrate how to use scanner data using tools from econometric and statistical analyses, including the limitations in interpreting results using the data - Describe and resolve key methodological issues related to using the data to facilitate more rapid analyses - Provide an overview of published literature as background for designing new studies - Demonstrate key applications of the data for food policy research
This book is about the role of expert systems in marketing, particularly in the consumer goods industry. Section I describes the changing nature of consumer marketing and presents the rationale and need for expert systems. The remainder of the book combines a tutorial on expert systems with a series of expert system prototypes. The tutorial material is presented in three places. First, section II is devoted to introducing expert systems in general. Chapter 3 provides a general introduction to the topic, which is continued in chapter 4 where a small expert system (the Promotion Advisor) is used to illustrate the important features of a backward-chaining, rule-based system. The promotion theme is extended in chapter 5 where a larger system is presented. The material in all three of these chapters was designed as an introduction and tutorial on the most common technology for building applied expert systems: the backward-chaining, rule-based inference engine. Tutorial material is also contained in the body of the chapters that describe the prototypes. This material is usually in the form of sample rules and a description of the process for applying the rules. The third location of the expert system material is in chapters that follow discussions of the prototypes. Chapter 7 is a technical chapter on the coupling of expert systems to traditional systems.
"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.
When grocery stores began using electronic scanners to capture prices paid for meat, it was assumed that the livestock industry could capitalize on having these point-of-sale data available as a measure of the value of its products. This report compares scanner price data (SPD) with publicly available data collected. Of the two data types, SPD provide more info. about retail meat markets, including a wider variety of meat-cut prices, multiple measures of an average price, the volume of sales, and the relative importance of discounted prices. The SPD sample, however, is not statistically drawn, and complicated processing requirements delay its release, which makes SPD less useful than other data for analyzing current market conditions. Illus.
This volume collects the extended versions of papers presented at the SIS Conference “Statistics and Data Science: new challenges, new generations”, held in Florence, Italy on June 28-30, 2017. Highlighting the central role of statistics and data analysis methods in the era of Data Science, the contributions offer an essential overview of the latest developments in various areas of statistics research. The 35 contributions have been divided into six parts, each of which focuses on a core area contributing to “Data Science”. The book covers topics including strong statistical methodologies, Bayesian approaches, applications in population and social studies, studies in economics and finance, techniques of sample design and mathematical statistics. Though the book is mainly intended for researchers interested in the latest frontiers of Statistics and Data Analysis, it also offers valuable supplementary material for students of the disciplines dealt with here. Lastly, it will help Statisticians and Data Scientists recognize their counterparts’ fundamental role.