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This book presents guidelines for the development and evaluation of statistical software designed to ensure minimum acceptable statistical functionality as well as ease of interpretation and use. It consists of the proceedings of a forum that focused on three qualities of statistical software: richnessâ€"the availability of layers of output sophistication, guidanceâ€"how the package helps a user do an analysis and do it well, and exactnessâ€"determining if the output is "correct" and when and how to warn of potential problems.
This timely, thoughtful book provides a clear introduction to using panel data in research. It describes the different types of panel datasets commonly used for empirical analysis, and how to use them for cross sectional, panel, and event history analysis. Longhi and Nandi then guide the reader through the data management and estimation process, including the interpretation of the results and the preparation of the final output tables. Using existing data sets and structured as hands-on exercises, each chapter engages with practical issues associated with using data in research. These include: Data cleaning Data preparation Computation of descriptive statistics Using sample weights Choosing and implementing the right estimator Interpreting results Preparing final output tables Graphical representation Written by experienced authors this exciting textbook provides the practical tools needed to use panel data in research.
In the last 20 years, econometric theory on panel data has developed rapidly, particularly for analyzing common behaviors among individuals over time. Meanwhile, the statistical methods employed by applied researchers have not kept up-to-date. This book attempts to fill in this gap by teaching researchers how to use the latest panel estimation methods correctly. Almost all applied economics articles use panel data or panel regressions. However, many empirical results from typical panel data analyses are not correctly executed. This book aims to help applied researchers to run panel regressions correctly and avoid common mistakes. The book explains how to model cross-sectional dependence, how...
This book identifies challenges and opportunities in the development and implementation of software that contain significant statistical content. While emphasizing the relevance of using rigorous statistical and probabilistic techniques in software engineering contexts, it presents opportunities for further research in the statistical sciences and their applications to software engineering. It is intended to motivate and attract new researchers from statistics and the mathematical sciences to attack relevant and pressing problems in the software engineering setting. It describes the "big picture," as this approach provides the context in which statistical methods must be developed. The book's survey nature is directed at the mathematical sciences audience, but software engineers should also find the statistical emphasis refreshing and stimulating. It is hoped that the book will have the effect of seeding the field of statistical software engineering by its indication of opportunities where statistical thinking can help to increase understanding, productivity, and quality of software and software production.