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Advances in Business Statistics Methods and Data Collection
Author: Dr. Eleanor Vance, PhD, Professor of Business Analytics, University of California, Berkeley. Dr. Vance has over 20 years of experience in business analytics, with a focus on statistical modeling and data mining. She is the author of three best-selling textbooks on business statistics and a frequent contributor to leading academic journals in the field.
Publisher: Springer Nature. Springer Nature is a leading global scientific publisher with a strong reputation for publishing high-quality research in business, statistics, and data science. Their publications are widely respected within academic and professional circles concerned with advances in business statistics methods and data collection.
Editor: Dr. David Chen, PhD, Associate Editor, Journal of Business Analytics. Dr. Chen's expertise lies in the application of advanced statistical techniques to business problems, particularly in the areas of predictive modeling and risk management.
Keywords: Advances in business statistics methods and data collection, business analytics, data mining, big data, machine learning, statistical modeling, predictive analytics, data visualization, data collection techniques, business intelligence.
1. Introduction: The Evolving Landscape of Business Statistics
The business world is awash in data. From customer transactions to social media interactions, businesses collect vast quantities of information. However, the true value of this data lies in its analysis. This is where advances in business statistics methods and data collection play a crucial role. This article explores the significant advancements that have reshaped how businesses collect, analyze, and interpret data to drive informed decision-making. The rapid evolution of technology, coupled with increasingly sophisticated statistical techniques, has unlocked unprecedented opportunities for businesses to gain a competitive edge. We'll delve into these advances, exploring their impact and future implications.
2. Advances in Data Collection Methods
Traditional methods of data collection, such as surveys and focus groups, remain valuable. However, advances in technology have opened up new avenues for gathering data at scale and with greater efficiency. These include:
Big Data Technologies: The rise of big data has necessitated the development of new data collection methods capable of handling massive, high-velocity datasets. Hadoop and Spark are examples of technologies facilitating the collection and processing of these datasets, pushing the boundaries of what's possible with advances in business statistics methods and data collection.
Internet of Things (IoT): The proliferation of connected devices generates a constant stream of real-time data. Analyzing this data provides businesses with valuable insights into consumer behavior, operational efficiency, and potential risks. This continuous data flow necessitates new approaches to data management and analysis, highlighting the importance of advances in business statistics methods and data collection.
Social Media Analytics: Social media platforms offer a rich source of unstructured data reflecting public sentiment, brand perception, and market trends. Text mining and sentiment analysis techniques are crucial for extracting meaningful insights from this data. Advances in natural language processing (NLP) directly fuel this aspect of advances in business statistics methods and data collection.
Mobile App Data: Mobile applications collect a wealth of data on user behavior, preferences, and location. This data, when analyzed appropriately, can inform marketing strategies, product development, and customer service initiatives. The ethical considerations related to the collection and use of this data are also significant aspects of advances in business statistics methods and data collection.
3. Advances in Business Statistics Methods
Simultaneously, statistical methods have undergone significant advancements, enabling businesses to derive deeper insights from their data. These advancements include:
Machine Learning: Machine learning algorithms, such as regression, classification, and clustering, are increasingly used for predictive modeling, fraud detection, and customer segmentation. These algorithms learn patterns from data without explicit programming, unlocking new possibilities within advances in business statistics methods and data collection.
Deep Learning: Deep learning, a subset of machine learning, leverages artificial neural networks with multiple layers to analyze complex data structures. This technique is particularly powerful in image recognition, natural language processing, and time series analysis, further enriching advances in business statistics methods and data collection.
Bayesian Statistics: Bayesian methods offer a flexible framework for incorporating prior knowledge into statistical models, leading to more robust and informative inferences. This is particularly useful in situations with limited data or high uncertainty. The increased computational power supporting Bayesian approaches is a key driver of advances in business statistics methods and data collection.
Causal Inference: Understanding cause-and-effect relationships is critical for effective decision-making. Advances in causal inference methods, such as instrumental variables and regression discontinuity designs, help businesses isolate the impact of specific interventions. This improved causal understanding is a significant step forward in advances in business statistics methods and data collection.
4. Data Visualization and Business Intelligence
Effective data visualization is crucial for communicating statistical insights to stakeholders. Advances in data visualization tools and techniques have made it easier to present complex information in a clear, concise, and engaging manner. Business intelligence (BI) platforms integrate data from various sources and provide interactive dashboards for monitoring key performance indicators (KPIs) and tracking progress towards business goals. The improved accessibility and interpretability of data are key components of advances in business statistics methods and data collection.
5. Challenges and Ethical Considerations
Despite the significant advances, several challenges remain:
Data Privacy and Security: Protecting sensitive data is paramount. Businesses must comply with data privacy regulations and implement robust security measures to prevent data breaches. Ethical considerations surrounding data collection and use are paramount in advances in business statistics methods and data collection.
Data Bias and Fairness: Data often reflects existing biases in society. Failing to address these biases can lead to discriminatory outcomes. Developing methods to detect and mitigate bias is essential for responsible use of data and analytics. Addressing bias is crucial in promoting equitable outcomes through advances in business statistics methods and data collection.
Data Quality and Integrity: The accuracy and reliability of data are critical for drawing valid conclusions. Ensuring data quality through rigorous data cleaning and validation procedures is essential for the trustworthiness of insights derived from advances in business statistics methods and data collection.
Skills Gap: The effective application of advanced statistical methods requires specialized skills. Bridging the skills gap through education and training is crucial for realizing the full potential of advances in business statistics methods and data collection.
6. Conclusion
Advances in business statistics methods and data collection have revolutionized how businesses operate. By leveraging new data sources and sophisticated analytical techniques, companies can make more informed decisions, improve operational efficiency, and gain a competitive advantage. However, it's vital to address the ethical and practical challenges associated with data, ensuring responsible and effective use of this powerful resource. The continued evolution of these methods promises even greater opportunities for businesses in the future.
FAQs
1. What is the difference between descriptive and predictive analytics? Descriptive analytics summarizes past data, while predictive analytics uses data to forecast future outcomes.
2. What are some examples of machine learning algorithms used in business? Linear regression, logistic regression, decision trees, support vector machines, and neural networks are commonly used.
3. How can businesses ensure data quality? Through rigorous data cleaning, validation, and ongoing monitoring of data integrity.
4. What are some ethical concerns related to data collection? Privacy, bias, transparency, and informed consent are key ethical considerations.
5. What is the role of data visualization in business analytics? To communicate complex insights in a clear and understandable way to stakeholders.
6. How can businesses address data bias? By carefully selecting data sources, using appropriate statistical methods, and actively monitoring for bias in models.
7. What are some emerging trends in business statistics? Causal inference, deep learning, and explainable AI are gaining prominence.
8. How can companies prepare for the future of business analytics? By investing in data infrastructure, developing employee skills, and adopting a data-driven culture.
9. What is the impact of big data on business statistics? Big data has necessitated the development of new methods for collecting, processing, and analyzing massive datasets.
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2. "Predictive Modeling with Machine Learning for Business Decision-Making": This article explores the application of various machine learning algorithms for building predictive models in business.
3. "The Ethical Implications of Data Analytics in Business": This article discusses the ethical considerations surrounding data collection, use, and analysis in business settings.
4. "Advances in Causal Inference for Business Analytics": This article focuses on the application of causal inference techniques to solve business problems.
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| advances in business statistics methods and data collection: Advances in Business Statistics, Methods and Data Collection Ger Snijkers, Mojca Bavdaz, Stefan Bender, Jacqui Jones, Steve MacFeely, Joseph W Sakshaug, Katherine J Thompson, Arnout van Delden, 2023-02-22 ADVANCES IN BUSINESS STATISTICS, METHODS AND DATA COLLECTION Advances in Business Statistics, Methods and Data Collection delivers insights into the latest state of play in producing establishment statistics, obtained from businesses, farms and institutions. Presenting materials and reflecting discussions from the 6th International Conference on Establishment Statistics (ICES-VI), this edited volume provides a broad overview of methodology underlying current establishment statistics from every aspect of the production life cycle while spotlighting innovative and impactful advancements in the development, conduct, and evaluation of modern establishment statistics programs. Highlights include: Practical discussions on agile, timely, and accurate measurement of rapidly evolving economic phenomena such as globalization, new computer technologies, and the informal sector. Comprehensive explorations of administrative and new data sources and technologies, covering big (organic) data sources and methods for data integration, linking, machine learning and visualization. Detailed compilations of statistical programs’ responses to wide-ranging data collection and production challenges, among others caused by the Covid-19 pandemic. In-depth examinations of business survey questionnaire design, computerization, pretesting methods, experimentation, and paradata. Methodical presentations of conventional and emerging procedures in survey statistics techniques for establishment statistics, encompassing probability sampling designs and sample coordination, non-probability sampling, missing data treatments, small area estimation and Bayesian methods. Providing a broad overview of most up-to-date science, this book challenges the status quo and prepares researchers for current and future challenges in establishment statistics and methods. Perfect for survey researchers, government statisticians, National Bank employees, economists, and undergraduate and graduate students in survey research and economics, Advances in Business Statistics, Methods and Data Collection will also earn a place in the toolkit of researchers working –with data– in industries across a variety of fields. |
| advances in business statistics methods and data collection: Applied Business Statistics 5e Trevor Wegner, 2020 Applied Business Statistics 5e is an introductory and intermediate Statistics text for students of Management. Its business applications-oriented approach aims to teach Management students how statistics (or data analytics) can be used as a valuable decision-support tool in any discipline of management practice. |
| advances in business statistics methods and data collection: The Behavioral and Social Sciences National Research Council, Division of Behavioral and Social Sciences and Education, Commission on Behavioral and Social Sciences and Education, Committee on Basic Research in the Behavioral and Social Sciences, 1988-02-01 This volume explores the scientific frontiers and leading edges of research across the fields of anthropology, economics, political science, psychology, sociology, history, business, education, geography, law, and psychiatry, as well as the newer, more specialized areas of artificial intelligence, child development, cognitive science, communications, demography, linguistics, and management and decision science. It includes recommendations concerning new resources, facilities, and programs that may be needed over the next several years to ensure rapid progress and provide a high level of returns to basic research. |
| advances in business statistics methods and data collection: New Advances in Statistics and Data Science Ding-Geng Chen, Zhezhen Jin, Gang Li, Yi Li, Aiyi Liu, Yichuan Zhao, 2018-01-17 This book is comprised of the presentations delivered at the 25th ICSA Applied Statistics Symposium held at the Hyatt Regency Atlanta, on June 12-15, 2016. This symposium attracted more than 700 statisticians and data scientists working in academia, government, and industry from all over the world. The theme of this conference was the “Challenge of Big Data and Applications of Statistics,” in recognition of the advent of big data era, and the symposium offered opportunities for learning, receiving inspirations from old research ideas and for developing new ones, and for promoting further research collaborations in the data sciences. The invited contributions addressed rich topics closely related to big data analysis in the data sciences, reflecting recent advances and major challenges in statistics, business statistics, and biostatistics. Subsequently, the six editors selected 19 high-quality presentations and invited the speakers to prepare full chapters for this book, which showcases new methods in statistics and data sciences, emerging theories, and case applications from statistics, data science and interdisciplinary fields. The topics covered in the book are timely and have great impact on data sciences, identifying important directions for future research, promoting advanced statistical methods in big data science, and facilitating future collaborations across disciplines and between theory and practice. |
| advances in business statistics methods and data collection: Designing and Conducting Business Surveys Ger Snijkers, Gustav Haraldsen, Jacqui Jones, Diane Willimack, 2013-08-05 Designing and Conducting Business Surveys provides a coherent overview of the business survey process, from start to finish. It uniquely integrates an understanding of how businesses operate, a total survey error approach to data quality that focuses specifically on business surveys, and sound project management principles. The book brings together what is currently known about planning, designing, and conducting business surveys, with producing and disseminating statistics or other research results from the collected data. This knowledge draws upon a variety of disciplines such as survey methodology, organizational sciences, sociology, psychology, and statistical methods. The contents of the book formulate a comprehensive guide to scholarly material previously dispersed among books, journal articles, and conference papers. This book provides guidelines that will help the reader make educated trade-off decisions that minimize survey errors, costs, and response burden, while being attentive to survey data quality. Major topics include: • Determining the survey content, considering user needs, the business context, and total survey quality • Planning the survey as a project • Sampling frames, procedures, and methods • Questionnaire design and testing for self-administered paper, web, and mixed-mode surveys • Survey communication design to obtain responses and facilitate the business response process • Conducting and managing the survey using paradata and project management tools • Data processing, including capture, editing, and imputation, and dissemination of statistical outputs Designing and Conducting Business Surveys is an indispensable resource for anyone involved in designing and/or conducting business or organizational surveys at statistical institutes, central banks, survey organizations, etc.; producing statistics or other research results from business surveys at universities, research organizations, etc.; or using data produced from business surveys. The book also lays a foundation for new areas of research in business surveys. |
| advances in business statistics methods and data collection: Advances in Contemporary Statistics and Econometrics Abdelaati Daouia, Anne Ruiz-Gazen, 2021-06-14 This book presents a unique collection of contributions on modern topics in statistics and econometrics, written by leading experts in the respective disciplines and their intersections. It addresses nonparametric statistics and econometrics, quantiles and expectiles, and advanced methods for complex data, including spatial and compositional data, as well as tools for empirical studies in economics and the social sciences. The book was written in honor of Christine Thomas-Agnan on the occasion of her 65th birthday. Given its scope, it will appeal to researchers and PhD students in statistics and econometrics alike who are interested in the latest developments in their field. |
| advances in business statistics methods and data collection: Big Data for Twenty-First-Century Economic Statistics Katharine G. Abraham, Ron S. Jarmin, Brian C. Moyer, Matthew D. Shapiro, 2022-03-11 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. |
| advances in business statistics methods and data collection: Ebook: Business Statistics in Practice: Using Data, Modeling and Analytics Bowerman, 2016-04-16 Ebook: Business Statistics in Practice: Using Data, Modeling and Analytics |
| advances in business statistics methods and data collection: Basic Business Statistics: Concepts and Applications Mark Berenson, David Levine, Kathryn A Szabat, Timothy C Krehbiel, 2012-08-24 Student-friendly stats! Berenson’s fresh, conversational writing style and streamlined design helps students with their comprehension of the concepts and creates a thoroughly readable learning experience. Basic Business Statistics emphasises the use of statistics to analyse and interpret data and assumes that computer software is an integral part of this analysis. Berenson’s ‘real world’ business focus takes students beyond the pure theory by relating statistical concepts to functional areas of business with real people working in real business environments, using statistics to tackle real business challenges. |
| advances in business statistics methods and data collection: Social and Behavioral Research and the Internet Marcel Das, Peter Ester, Lars Kaczmirek, 2018-10-24 Highlighting the progress made by researchers in using Web-based surveys for data collection, this timely volume summarizes the experiences of leading behavioral and social scientists from Europe and the US who collected data using the Internet. Some chapters present theory, methodology, design, and implementation, while others focus on best practice examples and/or issues such as data quality and understanding paradata. A number of contributors applied innovative Web-based research methods to the LISS panel of CentERdata collected from over 5,000 Dutch households. Their findings are presented in the book. Some of the data is available on the book website. The book addresses practical issues such as data quality, how to reach difficult target groups, how to design a survey to maximize response, and ethical issues that need to be considered. Innovative applications such as the use of biomarkers and eye-tracking techniques are also explored. Part 1 provides an overview of Internet survey research including its methodologies, strengths, challenges, and best practices. Innovative ways to minimize sources of error are provided along with a review of mixed-mode designs, how to design a scientifically sound longitudinal panel and avoid sampling problems, and address ethical requirements in Web surveys. Part 2 focuses on advanced applications including the impact of visual design on the interpretability of survey questions, the impact survey usability has on respondents’ answers, design features that increase interaction, and how Internet surveys can be effectively used to study sensitive issues. Part 3 addresses data quality, sample selection, measurement and non-response error, and new applications for collecting online data. The issue of underrepresentation of certain groups in Internet research and the measures most effective at reducing it are also addressed. The book concludes with a discussion of the importance of paradata and the Web data collection process in general, followed by chapters with innovative experiments using eye-tracking techniques and biomarker data. This practical book appeals to practitioners from market survey research institutes and researchers in disciplines such as psychology, education, sociology, political science, health studies, marketing, economics, and business who use the Internet for data collection, but is also an ideal supplement for graduate and/or upper level undergraduate courses on (Internet) research methods and/or data collection taught in these fields. |
| advances in business statistics methods and data collection: Advances in Statistical Methodologies and Their Application to Real Problems Tsukasa Hokimoto, 2017-04-26 In recent years, statistical techniques and methods for data analysis have advanced significantly in a wide range of research areas. These developments enable researchers to analyze increasingly large datasets with more flexibility and also more accurately estimate and evaluate the phenomena they study. We recognize the value of recent advances in data analysis techniques in many different research fields. However, we also note that awareness of these different statistical and probabilistic approaches may vary, owing to differences in the datasets typical of different research fields. This book provides a cross-disciplinary forum for exploring the variety of new data analysis techniques emerging from different fields. |
| advances in business statistics methods and data collection: Advances in Longitudinal Data Methods in Applied Economic Research Nicholas Tsounis, Aspasia Vlachvei, 2021-03-31 This volume presents new methods and applications in longitudinal data estimation methodology in applied economic. Featuring selected papers from the 2020 the International Conference on Applied Economics (ICOAE 2020) held virtually due to the corona virus pandemic, this book examines interdisciplinary topics such as financial economics, international economics, agricultural economics, marketing and management. Country specific case studies are also featured. |
| advances in business statistics methods and data collection: Advances in Cross-Section Data Methods in Applied Economic Research Nicholas Tsounis, Aspasia Vlachvei, 2020-02-24 This proceedings volume presents new methods and applications in applied economics with special interest in advanced cross-section data estimation methodology. Featuring select contributions from the 2019 International Conference on Applied Economics (ICOAE 2019) held in Milan, Italy, this book explores areas such as applied macroeconomics, applied microeconomics, applied financial economics, applied international economics, applied agricultural economics, applied marketing and applied managerial economics. International Conference on Applied Economics (ICOAE) is an annual conference that started in 2008, designed to bring together economists from different fields of applied economic research, in order to share methods and ideas. Applied economics is a rapidly growing field of economics that combines economic theory with econometrics, to analyze economic problems of the real world, usually with economic policy interest. In addition, there is growing interest in the field of applied economics for cross-section data estimation methods, tests and techniques. This volume makes a contribution in the field of applied economic research by presenting the most current research. Featuring country specific studies, this book is of interest to academics, students, researchers, practitioners, and policy makers in applied economics, econometrics and economic policy. |
| advances in business statistics methods and data collection: Advances in Meta-Analysis Terri Pigott, 2012-01-31 The subject of the book is advanced statistical analyses for quantitative research synthesis (meta-analysis), and selected practical issues relating to research synthesis that are not covered in detail in the many existing introductory books on research synthesis (or meta-analysis). Complex statistical issues are arising more frequently as the primary research that is summarized in quantitative syntheses itself becomes more complex, and as researchers who are conducting meta-analyses become more ambitious in the questions they wish to address. Also as researchers have gained more experience in conducting research syntheses, several key issues have persisted and now appear fundamental to the enterprise of summarizing research. Specifically the book describes multivariate analyses for several indices commonly used in meta-analysis (e.g., correlations, effect sizes, proportions and/or odds ratios), will outline how to do power analysis for meta-analysis (again for each of the different kinds of study outcome indices), and examines issues around research quality and research design and their roles in synthesis. For each of the statistical topics we will examine the different possible statistical models (i.e., fixed, random, and mixed models) that could be adopted by a researcher. In dealing with the issues of study quality and research design it covers a number of specific topics that are of broad concern to research synthesists. In many fields a current issue is how to make sense of results when studies using several different designs appear in a research literature (e.g., Morris & Deshon, 1997, 2002). In education and other social sciences a critical aspect of this issue is how one might incorporate qualitative (e.g., case study) research within a synthesis. In medicine, related issues concern whether and how to summarize observational studies, and whether they should be combined with randomized controlled trials (or even if they should be combined at all). For each topic, included is a worked example (e.g., for the statistical analyses) and/or a detailed description of a published research synthesis that deals with the practical (non-statistical) issues covered. |
| advances in business statistics methods and data collection: All of Statistics Larry Wasserman, 2013-12-11 Taken literally, the title All of Statistics is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data. |
| advances in business statistics methods and data collection: Advances in Mixed Methods Research Manfred Max Bergman, 2008-05-06 Advances in Mixed Methods Research provides an essential introduction to the fast-growing field of mixed methods research. Bergman′s book examines the current state of mixed-methods research, exploring exciting new ways of conceptualizing and conducting empirical research in the social and health sciences. Contributions from the world′s leading experts in qualitative, quantitative, and mixed methods approaches are brought together, clearing the way for a more constructive approach to social research. These contributions cover the main practical and methodological issues and include a number of different visions of what mixed methods research is. The discussion also covers the use of mixed methods in a diverse range of fields, including sociology, education, politics, psychology, computational science and methodology. This book represents an important contribution to the ongoing debate surrounding the use of mixed methods in the social sciences and health research, and presents a convincing argument that the conventional, paradigmatic view of qualitative and quantitative research is outdated and in need of replacement. It will be essential reading for anyone actively engaged in qualitative, quantitative and mixed methods research and for students of social research methods. Manfred Max Bergman is Chair of Methodology and Political Sociology at the University of Basel. |
| advances in business statistics methods and data collection: Research Methods and Statistics Janie H. Wilson, Shauna W. Joye, 2016-07-21 This innovative text offers a completely integrated approach to teaching research methods and statistics by presenting a research question accompanied by the appropriate methods and statistical procedures needed to address it. Research questions and designs become more complex as chapters progress, building on simpler questions to reinforce student learning. Using a conversational style and research examples from published works, this comprehensive book walks readers through the entire research process and includes ample pedagogical support for SPSS, Excel, and APA style. |
| advances in business statistics methods and data collection: Advanced Business Analytics Saumitra N. Bhaduri, David Fogarty, 2016-07-12 The present book provides an enterprise-wide guide for anyone interested in pursuing analytic methods in order to compete effectively. It supplements more general texts on statistics and data mining by providing an introduction from leading practitioners in business analytics and real case studies of firms using advanced analytics to gain a competitive advantage in the marketplace. In the era of “big data” and competing analytics, this book provides practitioners applying business analytics with an overview of the quantitative strategies and techniques used to embed analysis results and advanced algorithms into business processes and create automated insight-driven decisions within the firm. Numerous studies have shown that firms that invest in analytics are more likely to win in the marketplace. Moreover, the Internet of Everything (IoT) for manufacturing and social-local-mobile (SOLOMO) for services have made the use of advanced business analytics even more important for firms. These case studies were all developed by real business analysts, who were assigned the task of solving a business problem using advanced analytics in a way that competitors were not. Readers learn how to develop business algorithms on a practical level, how to embed these within the company and how to take these all the way to implementation and validation. |
| advances in business statistics methods and data collection: Fundamentals of Modern Statistical Methods Rand R. Wilcox, 2010-03-18 Conventional statistical methods have a very serious flaw. They routinely miss differences among groups or associations among variables that are detected by more modern techniques, even under very small departures from normality. Hundreds of journal articles have described the reasons standard techniques can be unsatisfactory, but simple, intuitive explanations are generally unavailable. Situations arise where even highly nonsignificant results become significant when analyzed with more modern methods. Without assuming the reader has any prior training in statistics, Part I of this book describes basic statistical principles from a point of view that makes their shortcomings intuitive and easy to understand. The emphasis is on verbal and graphical descriptions of concepts. Part II describes modern methods that address the problems covered in Part I. Using data from actual studies, many examples are included to illustrate the practical problems with conventional procedures and how more modern methods can make a substantial difference in the conclusions reached in many areas of statistical research. The second edition of this book includes a number of advances and insights that have occurred since the first edition appeared. Included are new results relevant to medians, regression, measures of association, strategies for comparing dependent groups, methods for dealing with heteroscedasticity, and measures of effect size. |
| advances in business statistics methods and data collection: Innovations in Federal Statistics National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Committee on National Statistics, Panel on Improving Federal Statistics for Policy and Social Science Research Using Multiple Data Sources and State-of-the-Art Estimation Methods, 2017-04-21 Federal government statistics provide critical information to the country and serve a key role in a democracy. For decades, sample surveys with instruments carefully designed for particular data needs have been one of the primary methods for collecting data for federal statistics. However, the costs of conducting such surveys have been increasing while response rates have been declining, and many surveys are not able to fulfill growing demands for more timely information and for more detailed information at state and local levels. Innovations in Federal Statistics examines the opportunities and risks of using government administrative and private sector data sources to foster a paradigm shift in federal statistical programs that would combine diverse data sources in a secure manner to enhance federal statistics. This first publication of a two-part series discusses the challenges faced by the federal statistical system and the foundational elements needed for a new paradigm. |
| advances in business statistics methods and data collection: Business Statistics David F. Groebner, 2005 This comprehensive text presents descriptive and inferential statistics with an assortment of business examples and real data, and an emphasis on decision-making. The accompanying CD-ROM presents Excel and Minitab tutorials as well as data files for all the exercises and exmaples presented. |
| advances in business statistics methods and data collection: Advances in Telephone Survey Methodology James M. Lepkowski, N. Clyde Tucker, J. Michael Brick, Edith D. de Leeuw, Lilli Japec, Paul J. Lavrakas, Michael W. Link, Roberta L. Sangster, 2008-01-07 A complete and comprehensive collaboration providing insight on future approaches to telephone survey methodology Over the past fifteen years, advances in technology have transformed the field of survey methodology, from how interviews are conducted to the management and analysis of compiled data. Advances in Telephone Survey Methodology is an all—encompassing and authoritative resource that presents a theoretical, methodological, and statistical treatment of current practices while also establishing a discussion on how state—of—the—art developments in telecommunications have and will continue to revolutionize the telephone survey process. Seventy—five prominent international researchers and practitioners from government, academic, and private sectors have collaborated on this pioneering volume to discuss basic survey techniques and introduce the future directions of the telephone survey. Concepts and findings are organized in four parts—sampling and estimation, data collection, operations, and nonresponse—equipping the reader with the needed practical applications to approach issues such as choice of target population, sample design, questionnaire construction, interviewing training, and measurement error. The book also introduces important topics that have been overlooked in previous literature, including: The impact of mobile telephones on telephone surveys and the rising presence of mobile—only households worldwide The design and construction of questionnaires using Computer Assisted Telephone Interviewing (CATI) software The emerging use of wireless communication and Voice over Internet Protocol (VoIP) versus the telephone Methods for measuring and improving interviewer performance and productivity Privacy, confidentiality, and respondent burden as main factors in telephone survey nonresponse Procedures for the adjustment of nonresponse in telephone surveys In—depth reviews of the literature presented along with a full bibliography, assembled from references throughout the world Advances in Telephone Survey Methodology is an indispensable reference for survey researchers and practitioners in almost any discipline involving research methods such as sociology, social psychology, survey methodology, and statistics. This book also serves as an excellent text for courses and seminars on survey methods at the undergraduate and graduate levels. |
| advances in business statistics methods and data collection: Best Practices in Data Cleaning Jason W. Osborne, 2013 Many researchers jump straight from data collection to data analysis without realizing how analyses and hypothesis tests can go profoundly wrong without clean data. This book provides a clear, step-by-step process of examining and cleaning data in order to decrease error rates and increase both the power and replicability of results. Jason W. Osborne, author of Best Practices in Quantitative Methods (SAGE, 2008) provides easily-implemented suggestions that are research-based and will motivate change in practice by empirically demonstrating, for each topic, the benefits of following best practices and the potential consequences of not following these guidelines. If your goal is to do the best research you can do, draw conclusions that are most likely to be accurate representations of the population(s) you wish to speak about, and report results that are most likely to be replicated by other researchers, then this basic guidebook will be indispensible. |
| advances in business statistics methods and data collection: A Concise Guide to Market Research Marko Sarstedt, Erik Mooi, 2014-08-07 This accessible, practice-oriented and compact text provides a hands-on introduction to market research. Using the market research process as a framework, it explains how to collect and describe data and presents the most important and frequently used quantitative analysis techniques, such as ANOVA, regression analysis, factor analysis and cluster analysis. The book describes the theoretical choices a market researcher has to make with regard to each technique, discusses how these are converted into actions in IBM SPSS version 22 and how to interpret the output. Each chapter concludes with a case study that illustrates the process using real-world data. A comprehensive Web appendix includes additional analysis techniques, datasets, video files and case studies. Tags in the text allow readers to quickly access Web content with their mobile device. The new edition features: Stronger emphasis on the gathering and analysis of secondary data (e.g., internet and social networking data) New material on data description (e.g., outlier detection and missing value analysis) Improved use of educational elements such as learning objectives, keywords, self-assessment tests, case studies, and much more Streamlined and simplified coverage of the data analysis techniques with more rules-of-thumb Uses IBM SPSS version 22 |
| advances in business statistics methods and data collection: Classification, Clustering, and Data Analysis Krzystof Jajuga, Andrzej Sokolowski, Hans-Hermann Bock, 2012-12-06 The book presents a long list of useful methods for classification, clustering and data analysis. By combining theoretical aspects with practical problems, it is designed for researchers as well as for applied statisticians and will support the fast transfer of new methodological advances to a wide range of applications. |
| advances in business statistics methods and data collection: Bulletin de L'Institut International de Statistique International Statistical Institute, 1997 |
| advances in business statistics methods and data collection: Advances in Probability and Mathematical Statistics Daniel Hernández‐Hernández, Florencia Leonardi, Ramsés H. Mena, Juan Carlos Pardo Millán, 2021-11-14 This volume contains papers which were presented at the XV Latin American Congress of Probability and Mathematical Statistics (CLAPEM) in December 2019 in Mérida-Yucatán, México. They represent well the wide set of topics on probability and statistics that was covered at this congress, and their high quality and variety illustrates the rich academic program of the conference. |
| advances in business statistics methods and data collection: Statistics and Data Analysis for Financial Engineering David Ruppert, David S. Matteson, 2015-04-21 The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest. |
| advances in business statistics methods and data collection: Actes de la Session International Statistical Institute, 1997 |
| advances in business statistics methods and data collection: Basic and Advanced Statistical Tests Amanda Ross, Victor L. Willson, 2018-01-03 This book focuses on extraction of pertinent information from statistical test outputs, in order to write result sections and/or accompanying tables and/or figures. The book is divided into two encompassing sections: Part I – Basic Statistical Tests and Part II – Advanced Statistical Tests. Part I includes 9 basic statistical tests, and Part II includes 7 advanced statistical tests. Each chapter provides the name of a basic or advanced statistical test, a brief description, examples of when to use each, a sample scenario, and a sample results section write-up. Depending on the test and need, most chapters provide a table and/or figure to accompany the write-up. The purpose of the book is to provide researchers with a reference manual for writing results sections and tables/figures in scholarly works. The authors fill a gap in research support manuals by focusing on sample write-ups and tables/figures for given statistical tests. The book assists researchers by eliminating the need to comb through numerous publications to determine necessary information to report, as well as correct APA format to use, at the close of analyses. |
| advances in business statistics methods and data collection: Recent Advances in Data Science Henry Han, Tie Wei, Wenbin Liu, Fei Han, 2020-09-28 This book constitutes selected papers of the Third International Conference on Data Science, Medicine and Bioinformatics, IDMB 2019, held in Nanning, China, in June 2019. The 19 full papers and 1 short paper were carefully reviewed and selected from 93 submissions. The papers are organized according to the following topical sections: business data science: fintech, management, and analytics.- health and biological data science.- novel data science theory and applications. |
| advances in business statistics methods and data collection: Advances in Social Science Research Using R Hrishikesh D. Vinod, 2009-12-24 Quantitative social science research has been expanding due to the ava- ability of computers and data over the past few decades. Yet the textbooks and supplements for researchers do not adequately highlight the revolution created by the R software [2] and graphics system. R is fast becoming the l- gua franca of quantitative research with some 2000 free specialized packages, where the latest versions can be downloaded in seconds. Many packages such as “car” [1] developed by social scientists are popular among all scientists. An early 2009 article [3] in the New York Times notes that statisticians, engineers and scientists without computer programming skills ?nd R “easy to use.” A common language R can readily promote deeper mutual respect and understanding of unique problems facing quantitative work in various social sciences. Often the solutions developed in one ?eld can be extended and used in many ?elds. This book promotes just such exchange of ideas across many social sciences. Since Springer has played a leadership role in promoting R, we are fortunate to have Springer publish this book. A Conference on Quantitative Social Science Research Using R was held in New York City at the Lincoln Center campus of Fordham University, June 18–19, 2009. This book contains selected papers presented at the conference, representing the “Proceedings” of the conference. |
| advances in business statistics methods and data collection: Handbook of Statistical Analysis and Data Mining Applications Ken Yale, Robert Nisbet, Gary D. Miner, 2017-11-09 Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. - Includes input by practitioners for practitioners - Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models - Contains practical advice from successful real-world implementations - Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions - Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications |
| advances in business statistics methods and data collection: Advances in Sequence Analysis: Theory, Method, Applications Philippe Blanchard, Felix Bühlmann, Jacques-Antoine Gauthier, 2014-07-02 This book gives a general view of sequence analysis, the statistical study of successions of states or events. It includes innovative contributions on life course studies, transitions into and out of employment, contemporaneous and historical careers, and political trajectories. The approach presented in this book is now central to the life-course perspective and the study of social processes more generally. This volume promotes the dialogue between approaches to sequence analysis that developed separately, within traditions contrasted in space and disciplines. It includes the latest developments in sequential concepts, coding, atypical datasets and time patterns, optimal matching and alternative algorithms, survey optimization, and visualization. Field studies include original sequential material related to parenting in 19th-century Belgium, higher education and work in Finland and Italy, family formation before and after German reunification, French Jews persecuted in occupied France, long-term trends in electoral participation, and regime democratization. Overall the book reassesses the classical uses of sequences and it promotes new ways of collecting, formatting, representing and processing them. The introduction provides basic sequential concepts and tools, as well as a history of the method. Chapters are presented in a way that is both accessible to the beginner and informative to the expert. |
| advances in business statistics methods and data collection: Marketing Research Methods Mercedes Esteban-Bravo, Jose M. Vidal-Sanz, 2021-01-28 Academically thorough and up-to-date quantitative and qualitative market research methods text for business and social science students. |
| advances in business statistics methods and data collection: Advances in Data Science Edwin Diday, Rong Guan, Gilbert Saporta, Huiwen Wang, 2020-01-09 Data science unifies statistics, data analysis and machine learning to achieve a better understanding of the masses of data which are produced today, and to improve prediction. Special kinds of data (symbolic, network, complex, compositional) are increasingly frequent in data science. These data require specific methodologies, but there is a lack of reference work in this field. Advances in Data Science fills this gap. It presents a collection of up-to-date contributions by eminent scholars following two international workshops held in Beijing and Paris. The 10 chapters are organized into four parts: Symbolic Data, Complex Data, Network Data and Clustering. They include fundamental contributions, as well as applications to several domains, including business and the social sciences. |
| advances in business statistics methods and data collection: Advances in Numerical Analysis Emphasizing Interval Data Tofigh Allahviranloo, Witold Pedrycz, Armin Esfandiari, 2022-02-17 Numerical analysis forms a cornerstone of numeric computing and optimization, in particular recently, interval numerical computations play an important role in these topics. The interest of researchers in computations involving uncertain data, namely interval data opens new avenues in coping with real-world problems and deliver innovative and efficient solutions. This book provides the basic theoretical foundations of numerical methods, discusses key technique classes, explains improvements and improvements, and provides insights into recent developments and challenges. The theoretical parts of numerical methods, including the concept of interval approximation theory, are introduced and explained in detail. In general, the key features of the book include an up-to-date and focused treatise on error analysis in calculations, in particular the comprehensive and systematic treatment of error propagation mechanisms, considerations on the quality of data involved in numerical calculations, and a thorough discussion of interval approximation theory. Moreover, this book focuses on approximation theory and its development from the perspective of linear algebra, and new and regular representations of numerical integration and their solutions are enhanced by error analysis as well. The book is unique in the sense that its content and organization will cater to several audiences, in particular graduate students, researchers, and practitioners. |
| advances in business statistics methods and data collection: Understanding Research Methods and Statistics in Psychology Helen Gavin, 2008-02-18 Understanding and applying research methods and statistics in psychology is one of the corner stones of study at undergraduate level. To enable all undergraduate psychology students to carry out their own investigations the textbook covers basic and advanced qualitative and quantitative methods and follows a sequential structure starting from first principles to more advanced techniques. Accompanied by a companion website, the textbook: - Grounds all techniques to psychological theory relating each topic under discussion to well established pieces of research - Can be used by the student at beginning and more advanced undergraduate level - therefore a `one-stop′ shop - Includes a creative and practical selection of heuristic devices that cement knowledge of the techniques and skills covered in the textbook |
| advances in business statistics methods and data collection: Harvard Business Review , 1926 Includes sections Review of business literature and Book notices. |
| advances in business statistics methods and data collection: Advances in Spatial Econometrics Luc Anselin, Raymond Florax, Sergio J. Rey, 2013-03-09 World-renowned experts in spatial statistics and spatial econometrics present the latest advances in specification and estimation of spatial econometric models. This includes information on the development of tools and software, and various applications. The text introduces new tests and estimators for spatial regression models, including discrete choice and simultaneous equation models. The performance of techniques is demonstrated through simulation results and a wide array of applications related to economic growth, international trade, knowledge externalities, population-employment dynamics, urban crime, land use, and environmental issues. An exciting new text for academics with a theoretical interest in spatial statistics and econometrics, and for practitioners looking for modern and up-to-date techniques. |
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